Migrating encoded data slices in a dispersed storage network

ABSTRACT

A method begins by a processing module of a dispersed storage network (DSN) identifying a change in DSN memory of the DSN. For a set of encoded data slices effected by the change, the method continues with the processing module ascertaining updated properties of the DSN memory and performing an updating scoring function using properties of DSN access information and the updated properties of the DSN memory to produce an updated storage scoring resultant. The method continues with the processing module utilizing the updated storage scoring resultant to identify an updated set of storage units affiliated with a given storage pool of a plurality of storage pools of the DSN memory and sending at least one data migration request to at least one storage unit of the updated set of storage units regarding migration of at least one encoded data slice of the set of encoded data slices.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/019,074,entitled “UTILIZING A DECENTRALIZED AGREEMENT PROTOCOL IN A DISPERSEDSTORAGE NETWORK”, filed Jun. 30, 2014, which is hereby incorporatedherein by reference in its entirety and made part of the present U.S.Utility Patent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

NOT APPLICABLE

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

NOT APPLICABLE

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersed storage of data and distributed taskprocessing of data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc., on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a diagram of an example of a distributed storage and taskprocessing in accordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 5 is a logic diagram of an example of a method for outbound DSTprocessing in accordance with the present invention;

FIG. 6 is a schematic block diagram of an embodiment of a dispersederror encoding in accordance with the present invention;

FIG. 7 is a diagram of an example of a segment processing of thedispersed error encoding in accordance with the present invention;

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding in accordance with thepresent invention;

FIG. 9 is a diagram of an example of grouping selection processing ofthe outbound DST processing in accordance with the present invention;

FIG. 10 is a diagram of an example of converting data into slice groupsin accordance with the present invention;

FIG. 11 is a schematic block diagram of an embodiment of a DST executionunit in accordance with the present invention;

FIG. 12 is a schematic block diagram of an example of operation of a DSTexecution unit in accordance with the present invention;

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 14 is a logic diagram of an example of a method for inbound DSTprocessing in accordance with the present invention;

FIG. 15 is a diagram of an example of de-grouping selection processingof the inbound DST processing in accordance with the present invention;

FIG. 16 is a schematic block diagram of an embodiment of a dispersederror decoding in accordance with the present invention;

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of the dispersed error decoding in accordance with thepresent invention;

FIG. 18 is a diagram of an example of a de-segment processing of thedispersed error decoding in accordance with the present invention;

FIG. 19 is a diagram of an example of converting slice groups into datain accordance with the present invention;

FIG. 20 is a diagram of an example of a distributed storage within thedistributed computing system in accordance with the present invention;

FIG. 21 is a schematic block diagram of an example of operation ofoutbound distributed storage and/or task (DST) processing for storingdata in accordance with the present invention;

FIG. 22 is a schematic block diagram of an example of a dispersed errorencoding for the example of FIG. 21 in accordance with the presentinvention;

FIG. 23 is a diagram of an example of converting data into pillar slicegroups for storage in accordance with the present invention;

FIG. 24 is a schematic block diagram of an example of a storageoperation of a DST execution unit in accordance with the presentinvention;

FIG. 25 is a schematic block diagram of an example of operation ofinbound distributed storage and/or task (DST) processing for retrievingdispersed error encoded data in accordance with the present invention;

FIG. 26 is a schematic block diagram of an example of a dispersed errordecoding for the example of FIG. 25 in accordance with the presentinvention;

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing a plurality ofdata and a plurality of task codes in accordance with the presentinvention;

FIG. 28 is a schematic block diagram of an example of the distributedcomputing system performing tasks on stored data in accordance with thepresent invention;

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module facilitating the example of FIG. 28 in accordancewith the present invention;

FIG. 30 is a diagram of a specific example of the distributed computingsystem performing tasks on stored data in accordance with the presentinvention;

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30 in accordance with the presentinvention;

FIG. 32 is a diagram of an example of DST allocation information for theexample of FIG. 30 in accordance with the present invention;

FIGS. 33-38 are schematic block diagrams of the DSTN module performingthe example of FIG. 30 in accordance with the present invention;

FIG. 39 is a diagram of an example of combining result information intofinal results for the example of FIG. 30 in accordance with the presentinvention;

FIG. 40A is a schematic block diagram of an embodiment of adecentralized agreement module in accordance with the present invention;

FIG. 40B is a flowchart illustrating an example of selecting a resourcein accordance with the present invention;

FIG. 41A is a schematic block diagram of another embodiment of adecentralized agreement module in accordance with the present invention;

FIG. 41B is a diagram of an embodiment of a location tree structure inaccordance with the present invention;

FIG. 41C is a flowchart illustrating another example of selecting aresource in accordance with the present invention;

FIG. 42A is a diagram of another embodiment of a location tree structureillustrating an example of modifying a resource pool in accordance withthe present invention;

FIG. 42B is a diagram of another embodiment of a location tree structureillustrating another example of modifying a resource pool in accordancewith the present invention;

FIG. 42C is a diagram of another embodiment of a location tree structureillustrating another example of modifying a resource pool in accordancewith the present invention;

FIG. 42D is a flowchart illustrating an example of modifying a resourcepool in accordance of the present invention;

FIGS. 43A and 43D are a schematic block diagram of an embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIGS. 43B and 43C are a schematic block diagram of another embodiment ofa distributed storage and task (DST) processing unit in accordance withthe present invention;

FIG. 43E is a flowchart illustrating an example of accessing a dispersedstorage network (DSN) memory in accordance with the present invention;

FIG. 44A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 44B is a schematic block diagram of another embodiment of adistributed storage and task (DST) processing unit in accordance withthe present invention;

FIG. 44C is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 44D is a schematic block diagram of another embodiment of adistributed storage and task (DST) processing unit in accordance withthe present invention;

FIG. 44E is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 44F is a schematic block diagram of another embodiment of adistributed storage and task (DST) processing unit in accordance withthe present invention;

FIG. 44G is a flowchart illustrating an example of migrating encodeddata slices in a dispersed storage network (DSN) memory in accordancewith the present invention;

FIG. 45A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 45B is a flowchart illustrating an example of recovering a storedencoded data slice in accordance with the present invention;

FIG. 46A is a schematic block diagram of another embodiment of adecentralized agreement module in accordance with the present invention;

FIG. 46B is a flowchart illustrating another example of selecting aresource in accordance with the present invention;

FIG. 47A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 47B is a flowchart illustrating another example of selecting aresource in accordance with the present invention;

FIG. 48A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention; and

FIG. 48B is a flowchart illustrating an example of selecting an accessresource in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system 10 that includes a user device 12 and/or a user device14, a distributed storage and/or task (DST) processing unit 16, adistributed storage and/or task network (DSTN) managing unit 18, a DSTintegrity processing unit 20, and a distributed storage and/or tasknetwork (DSTN) module 22. The components of the distributed computingsystem 10 are coupled via a network 24, which may include one or morewireless and/or wire lined communication systems; one or more privateintranet systems and/or public internet systems; and/or one or morelocal area networks (LAN) and/or wide area networks (WAN).

The DSTN module 22 includes a plurality of distributed storage and/ortask (DST) execution units 36 that may be located at geographicallydifferent sites (e.g., one in Chicago, one in Milwaukee, etc.). Each ofthe DST execution units is operable to store dispersed error encodeddata and/or to execute, in a distributed manner, one or more tasks ondata. The tasks may be a simple function (e.g., a mathematical function,a logic function, an identify function, a find function, a search enginefunction, a replace function, etc.), a complex function (e.g.,compression, human and/or computer language translation, text-to-voiceconversion, voice-to-text conversion, etc.), multiple simple and/orcomplex functions, one or more algorithms, one or more applications,etc.

Each of the user devices 12-14, the DST processing unit 16, the DSTNmanaging unit 18, and the DST integrity processing unit 20 include acomputing core 26 and may be a portable computing device and/or a fixedcomputing device. A portable computing device may be a social networkingdevice, a gaming device, a cell phone, a smart phone, a personal digitalassistant, a digital music player, a digital video player, a laptopcomputer, a handheld computer, a tablet, a video game controller, and/orany other portable device that includes a computing core. A fixedcomputing device may be a personal computer (PC), a computer server, acable set-top box, a satellite receiver, a television set, a printer, afax machine, home entertainment equipment, a video game console, and/orany type of home or office computing equipment. User device 12 and DSTprocessing unit 16 are configured to include a DST client module 34.

With respect to interfaces, each interface 30, 32, and 33 includessoftware and/or hardware to support one or more communication links viathe network 24 indirectly and/or directly. For example, interfaces 30support a communication link (e.g., wired, wireless, direct, via a LAN,via the network 24, etc.) between user device 14 and the DST processingunit 16. As another example, interface 32 supports communication links(e.g., a wired connection, a wireless connection, a LAN connection,and/or any other type of connection to/from the network 24) between userdevice 12 and the DSTN module 22 and between the DST processing unit 16and the DSTN module 22. As yet another example, interface 33 supports acommunication link for each of the DSTN managing unit 18 and DSTintegrity processing unit 20 to the network 24.

The distributed computing system 10 is operable to support dispersedstorage (DS) error encoded data storage and retrieval, to supportdistributed task processing on received data, and/or to supportdistributed task processing on stored data. In general and with respectto DS error encoded data storage and retrieval, the distributedcomputing system 10 supports three primary operations: storagemanagement, data storage and retrieval (an example of which will bediscussed with reference to FIGS. 20-26), and data storage integrityverification. In accordance with these three primary functions, data canbe encoded, distributedly stored in physically different locations, andsubsequently retrieved in a reliable and secure manner. Such a system istolerant of a significant number of failures (e.g., up to a failurelevel, which may be greater than or equal to a pillar width minus adecode threshold minus one) that may result from individual storagedevice failures and/or network equipment failures without loss of dataand without the need for a redundant or backup copy. Further, the systemallows the data to be stored for an indefinite period of time withoutdata loss and does so in a secure manner (e.g., the system is veryresistant to attempts at hacking the data).

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a user device 12-14. For instance, if asecond type of user device 14 has data 40 to store in the DSTN module22, it sends the data 40 to the DST processing unit 16 via its interface30. The interface 30 functions to mimic a conventional operating system(OS) file system interface (e.g., network file system (NFS), flash filesystem (FFS), disk file system (DFS), file transfer protocol (FTP),web-based distributed authoring and versioning (WebDAV), etc.) and/or ablock memory interface (e.g., small computer system interface (SCSI),internet small computer system interface (iSCSI), etc.). In addition,the interface 30 may attach a user identification code (ID) to the data40.

To support storage management, the DSTN managing unit 18 performs DSmanagement services. One such DS management service includes the DSTNmanaging unit 18 establishing distributed data storage parameters (e.g.,vault creation, distributed storage parameters, security parameters,billing information, user profile information, etc.) for a user device12-14 individually or as part of a group of user devices. For example,the DSTN managing unit 18 coordinates creation of a vault (e.g., avirtual memory block) within memory of the DSTN module 22 for a userdevice, a group of devices, or for public access and establishes pervault dispersed storage (DS) error encoding parameters for a vault. TheDSTN managing unit 18 may facilitate storage of DS error encodingparameters for each vault of a plurality of vaults by updating registryinformation for the distributed computing system 10. The facilitatingincludes storing updated registry information in one or more of the DSTNmodule 22, the user device 12, the DST processing unit 16, and the DSTintegrity processing unit 20.

The DS error encoding parameters (e.g., or dispersed storage errorcoding parameters) include data segmenting information (e.g., how manysegments data (e.g., a file, a group of files, a data block, etc.) isdivided into), segment security information (e.g., per segmentencryption, compression, integrity checksum, etc.), error codinginformation (e.g., pillar width, decode threshold, read threshold, writethreshold, etc.), slicing information (e.g., the number of encoded dataslices that will be created for each data segment); and slice securityinformation (e.g., per encoded data slice encryption, compression,integrity checksum, etc.).

The DSTN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSTN module 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSTN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a private vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

Another DS management service includes the DSTN managing unit 18performing network operations, network administration, and/or networkmaintenance. Network operations includes authenticating user dataallocation requests (e.g., read and/or write requests), managingcreation of vaults, establishing authentication credentials for userdevices, adding/deleting components (e.g., user devices, DST executionunits, and/or DST processing units) from the distributed computingsystem 10, and/or establishing authentication credentials for DSTexecution units 36. Network administration includes monitoring devicesand/or units for failures, maintaining vault information, determiningdevice and/or unit activation status, determining device and/or unitloading, and/or determining any other system level operation thataffects the performance level of the system 10. Network maintenanceincludes facilitating replacing, upgrading, repairing, and/or expandinga device and/or unit of the system 10.

To support data storage integrity verification within the distributedcomputing system 10, the DST integrity processing unit 20 performsrebuilding of ‘bad’ or missing encoded data slices. At a high level, theDST integrity processing unit 20 performs rebuilding by periodicallyattempting to retrieve/list encoded data slices, and/or slice names ofthe encoded data slices, from the DSTN module 22. For retrieved encodedslices, they are checked for errors due to data corruption, outdatedversion, etc. If a slice includes an error, it is flagged as a ‘bad’slice. For encoded data slices that were not received and/or not listed,they are flagged as missing slices. Bad and/or missing slices aresubsequently rebuilt using other retrieved encoded data slices that aredeemed to be good slices to produce rebuilt slices. The rebuilt slicesare stored in memory of the DSTN module 22. Note that the DST integrityprocessing unit 20 may be a separate unit as shown, it may be includedin the DSTN module 22, it may be included in the DST processing unit 16,and/or distributed among the DST execution units 36.

To support distributed task processing on received data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task processing) management and DST execution on received data(an example of which will be discussed with reference to FIGS. 3-19).With respect to the storage portion of the DST management, the DSTNmanaging unit 18 functions as previously described. With respect to thetasking processing of the DST management, the DSTN managing unit 18performs distributed task processing (DTP) management services. One suchDTP management service includes the DSTN managing unit 18 establishingDTP parameters (e.g., user-vault affiliation information, billinginformation, user-task information, etc.) for a user device 12-14individually or as part of a group of user devices.

Another DTP management service includes the DSTN managing unit 18performing DTP network operations, network administration (which isessentially the same as described above), and/or network maintenance(which is essentially the same as described above). Network operationsinclude, but are not limited to, authenticating user task processingrequests (e.g., valid request, valid user, etc.), authenticating resultsand/or partial results, establishing DTP authentication credentials foruser devices, adding/deleting components (e.g., user devices, DSTexecution units, and/or DST processing units) from the distributedcomputing system, and/or establishing DTP authentication credentials forDST execution units.

To support distributed task processing on stored data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task) management and DST execution on stored data. With respectto the DST execution on stored data, if the second type of user device14 has a task request 38 for execution by the DSTN module 22, it sendsthe task request 38 to the DST processing unit 16 via its interface 30.An example of DST execution on stored data will be discussed in greaterdetail with reference to FIGS. 27-39. With respect to the DSTmanagement, it is substantially similar to the DST management to supportdistributed task processing on received data.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSTN interface module 76.

The DSTN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). TheDSTN interface module 76 and/or the network interface module 70 mayfunction as the interface 30 of the user device 14 of FIG. 1. Furthernote that the IO device interface module 62 and/or the memory interfacemodules may be collectively or individually referred to as IO ports.

FIG. 3 is a diagram of an example of the distributed computing systemperforming a distributed storage and task processing operation. Thedistributed computing system includes a DST (distributed storage and/ortask) client module 34 (which may be in user device 14 and/or in DSTprocessing unit 16 of FIG. 1), a network 24, a plurality of DSTexecution units 1-n that includes two or more DST execution units 36 ofFIG. 1 (which form at least a portion of DSTN module 22 of FIG. 1), aDST managing module (not shown), and a DST integrity verification module(not shown). The DST client module 34 includes an outbound DSTprocessing section 80 and an inbound DST processing section 82. Each ofthe DST execution units 1-n includes a controller 86, a processingmodule 84, memory 88, a DT (distributed task) execution module 90, and aDST client module 34.

In an example of operation, the DST client module 34 receives data 92and one or more tasks 94 to be performed upon the data 92. The data 92may be of any size and of any content, where, due to the size (e.g.,greater than a few Terabytes), the content (e.g., secure data, etc.),and/or task(s) (e.g., MIPS intensive), distributed processing of thetask(s) on the data is desired. For example, the data 92 may be one ormore digital books, a copy of a company's emails, a large-scale Internetsearch, a video security file, one or more entertainment video files(e.g., television programs, movies, etc.), data files, and/or any otherlarge amount of data (e.g., greater than a few Terabytes).

Within the DST client module 34, the outbound DST processing section 80receives the data 92 and the task(s) 94. The outbound DST processingsection 80 processes the data 92 to produce slice groupings 96. As anexample of such processing, the outbound DST processing section 80partitions the data 92 into a plurality of data partitions. For eachdata partition, the outbound DST processing section 80 dispersed storage(DS) error encodes the data partition to produce encoded data slices andgroups the encoded data slices into a slice grouping 96. In addition,the outbound DST processing section 80 partitions the task 94 intopartial tasks 98, where the number of partial tasks 98 may correspond tothe number of slice groupings 96.

The outbound DST processing section 80 then sends, via the network 24,the slice groupings 96 and the partial tasks 98 to the DST executionunits 1-n of the DSTN module 22 of FIG. 1. For example, the outbound DSTprocessing section 80 sends slice group 1 and partial task 1 to DSTexecution unit 1. As another example, the outbound DST processingsection 80 sends slice group #n and partial task #n to DST executionunit #n.

Each DST execution unit performs its partial task 98 upon its slicegroup 96 to produce partial results 102. For example, DST execution unit#1 performs partial task #1 on slice group #1 to produce a partialresult #1, for results. As a more specific example, slice group #1corresponds to a data partition of a series of digital books and thepartial task #1 corresponds to searching for specific phrases, recordingwhere the phrase is found, and establishing a phrase count. In this morespecific example, the partial result #1 includes information as to wherethe phrase was found and includes the phrase count.

Upon completion of generating their respective partial results 102, theDST execution units send, via the network 24, their partial results 102to the inbound DST processing section 82 of the DST client module 34.The inbound DST processing section 82 processes the received partialresults 102 to produce a result 104. Continuing with the specificexample of the preceding paragraph, the inbound DST processing section82 combines the phrase count from each of the DST execution units 36 toproduce a total phrase count. In addition, the inbound DST processingsection 82 combines the ‘where the phrase was found’ information fromeach of the DST execution units 36 within their respective datapartitions to produce ‘where the phrase was found’ information for theseries of digital books.

In another example of operation, the DST client module 34 requestsretrieval of stored data within the memory of the DST execution units 36(e.g., memory of the DSTN module). In this example, the task 94 isretrieve data stored in the memory of the DSTN module. Accordingly, theoutbound DST processing section 80 converts the task 94 into a pluralityof partial tasks 98 and sends the partial tasks 98 to the respective DSTexecution units 1-n.

In response to the partial task 98 of retrieving stored data, a DSTexecution unit 36 identifies the corresponding encoded data slices 100and retrieves them. For example, DST execution unit #1 receives partialtask #1 and retrieves, in response thereto, retrieved slices #1. The DSTexecution units 36 send their respective retrieved slices 100 to theinbound DST processing section 82 via the network 24.

The inbound DST processing section 82 converts the retrieved slices 100into data 92. For example, the inbound DST processing section 82de-groups the retrieved slices 100 to produce encoded slices per datapartition. The inbound DST processing section 82 then DS error decodesthe encoded slices per data partition to produce data partitions. Theinbound DST processing section 82 de-partitions the data partitions torecapture the data 92.

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module 34 FIG. 1 coupled to a DSTN module 22 of a FIG. 1 (e.g., aplurality of n DST execution units 36) via a network 24. The outboundDST processing section 80 includes a data partitioning module 110, adispersed storage (DS) error encoding module 112, a grouping selectormodule 114, a control module 116, and a distributed task control module118.

In an example of operation, the data partitioning module 110 partitionsdata 92 into a plurality of data partitions 120. The number ofpartitions and the size of the partitions may be selected by the controlmodule 116 via control 160 based on the data 92 (e.g., its size, itscontent, etc.), a corresponding task 94 to be performed (e.g., simple,complex, single step, multiple steps, etc.), DS encoding parameters(e.g., pillar width, decode threshold, write threshold, segment securityparameters, slice security parameters, etc.), capabilities of the DSTexecution units 36 (e.g., processing resources, availability ofprocessing recourses, etc.), and/or as may be inputted by a user, systemadministrator, or other operator (human or automated). For example, thedata partitioning module 110 partitions the data 92 (e.g., 100Terabytes) into 100,000 data segments, each being 1 Gigabyte in size.Alternatively, the data partitioning module 110 partitions the data 92into a plurality of data segments, where some of data segments are of adifferent size, are of the same size, or a combination thereof.

The DS error encoding module 112 receives the data partitions 120 in aserial manner, a parallel manner, and/or a combination thereof. For eachdata partition 120, the DS error encoding module 112 DS error encodesthe data partition 120 in accordance with control information 160 fromthe control module 116 to produce encoded data slices 122. The DS errorencoding includes segmenting the data partition into data segments,segment security processing (e.g., encryption, compression,watermarking, integrity check (e.g., CRC), etc.), error encoding,slicing, and/or per slice security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC), etc.). Thecontrol information 160 indicates which steps of the DS error encodingare active for a given data partition and, for active steps, indicatesthe parameters for the step. For example, the control information 160indicates that the error encoding is active and includes error encodingparameters (e.g., pillar width, decode threshold, write threshold, readthreshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 122 of a datapartition into a set of slice groupings 96. The number of slicegroupings corresponds to the number of DST execution units 36 identifiedfor a particular task 94. For example, if five DST execution units 36are identified for the particular task 94, the group selecting modulegroups the encoded slices 122 of a data partition into five slicegroupings 96. The grouping selector module 114 outputs the slicegroupings 96 to the corresponding DST execution units 36 via the network24.

The distributed task control module 118 receives the task 94 andconverts the task 94 into a set of partial tasks 98. For example, thedistributed task control module 118 receives a task to find where in thedata (e.g., a series of books) a phrase occurs and a total count of thephrase usage in the data. In this example, the distributed task controlmodule 118 replicates the task 94 for each DST execution unit 36 toproduce the partial tasks 98. In another example, the distributed taskcontrol module 118 receives a task to find where in the data a firstphrase occurs, where in the data a second phrase occurs, and a totalcount for each phrase usage in the data. In this example, thedistributed task control module 118 generates a first set of partialtasks 98 for finding and counting the first phase and a second set ofpartial tasks for finding and counting the second phrase. Thedistributed task control module 118 sends respective first and/or secondpartial tasks 98 to each DST execution unit 36.

FIG. 5 is a logic diagram of an example of a method for outbounddistributed storage and task (DST) processing that begins at step 126where a DST client module receives data and one or more correspondingtasks. The method continues at step 128 where the DST client moduledetermines a number of DST units to support the task for one or moredata partitions. For example, the DST client module may determine thenumber of DST units to support the task based on the size of the data,the requested task, the content of the data, a predetermined number(e.g., user indicated, system administrator determined, etc.), availableDST units, capability of the DST units, and/or any other factorregarding distributed task processing of the data. The DST client modulemay select the same DST units for each data partition, may selectdifferent DST units for the data partitions, or a combination thereof.

The method continues at step 130 where the DST client module determinesprocessing parameters of the data based on the number of DST unitsselected for distributed task processing. The processing parametersinclude data partitioning information, DS encoding parameters, and/orslice grouping information. The data partitioning information includes anumber of data partitions, size of each data partition, and/ororganization of the data partitions (e.g., number of data blocks in apartition, the size of the data blocks, and arrangement of the datablocks). The DS encoding parameters include segmenting information,segment security information, error encoding information (e.g.,dispersed storage error encoding function parameters including one ormore of pillar width, decode threshold, write threshold, read threshold,generator matrix), slicing information, and/or per slice securityinformation. The slice grouping information includes informationregarding how to arrange the encoded data slices into groups for theselected DST units. As a specific example, if the DST client moduledetermines that five DST units are needed to support the task, then itdetermines that the error encoding parameters include a pillar width offive and a decode threshold of three.

The method continues at step 132 where the DST client module determinestask partitioning information (e.g., how to partition the tasks) basedon the selected DST units and data processing parameters. The dataprocessing parameters include the processing parameters and DST unitcapability information. The DST unit capability information includes thenumber of DT (distributed task) execution units, execution capabilitiesof each DT execution unit (e.g., MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or and the other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.)), and/or any information germane toexecuting one or more tasks.

The method continues at step 134 where the DST client module processesthe data in accordance with the processing parameters to produce slicegroupings. The method continues at step 136 where the DST client modulepartitions the task based on the task partitioning information toproduce a set of partial tasks. The method continues at step 138 wherethe DST client module sends the slice groupings and the correspondingpartial tasks to respective DST units.

FIG. 6 is a schematic block diagram of an embodiment of the dispersedstorage (DS) error encoding module 112 of an outbound distributedstorage and task (DST) processing section. The DS error encoding module112 includes a segment processing module 142, a segment securityprocessing module 144, an error encoding module 146, a slicing module148, and a per slice security processing module 150. Each of thesemodules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receives adata partition 120 from a data partitioning module and receivessegmenting information as the control information 160 from the controlmodule 116. The segmenting information indicates how the segmentprocessing module 142 is to segment the data partition 120. For example,the segmenting information indicates how many rows to segment the databased on a decode threshold of an error encoding scheme, indicates howmany columns to segment the data into based on a number and size of datablocks within the data partition 120, and indicates how many columns toinclude in a data segment 152. The segment processing module 142segments the data 120 into data segments 152 in accordance with thesegmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., cyclic redundancy check(CRC), etc.), and/or any other type of digital security. For example,when the segment security processing module 144 is enabled, it maycompress a data segment 152, encrypt the compressed data segment, andgenerate a CRC value for the encrypted data segment to produce a securedata segment 154. When the segment security processing module 144 is notenabled, it passes the data segments 152 to the error encoding module146 or is bypassed such that the data segments 152 are provided to theerror encoding module 146.

The error encoding module 146 encodes the secure data segments 154 inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters (e.g., also referred to as dispersed storage errorcoding parameters) include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an online coding algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction encodingparameters identify a specific error correction encoding scheme,specifies a pillar width of five, and specifies a decode threshold ofthree. From these parameters, the error encoding module 146 encodes adata segment 154 to produce an encoded data segment 156.

The slicing module 148 slices the encoded data segment 156 in accordancewith the pillar width of the error correction encoding parametersreceived as control information 160. For example, if the pillar width isfive, the slicing module 148 slices an encoded data segment 156 into aset of five encoded data slices. As such, for a plurality of encodeddata segments 156 for a given data partition, the slicing module outputsa plurality of sets of encoded data slices 158.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice 158 based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it compresses anencoded data slice 158, encrypts the compressed encoded data slice, andgenerates a CRC value for the encrypted encoded data slice to produce asecure encoded data slice 122. When the per slice security processingmodule 150 is not enabled, it passes the encoded data slices 158 or isbypassed such that the encoded data slices 158 are the output of the DSerror encoding module 112. Note that the control module 116 may beomitted and each module stores its own parameters.

FIG. 7 is a diagram of an example of a segment processing of a dispersedstorage (DS) error encoding module. In this example, a segmentprocessing module 142 receives a data partition 120 that includes 45data blocks (e.g., d1-d45), receives segmenting information (i.e.,control information 160) from a control module, and segments the datapartition 120 in accordance with the control information 160 to producedata segments 152. Each data block may be of the same size as other datablocks or of a different size. In addition, the size of each data blockmay be a few bytes to megabytes of data. As previously mentioned, thesegmenting information indicates how many rows to segment the datapartition into, indicates how many columns to segment the data partitioninto, and indicates how many columns to include in a data segment.

In this example, the decode threshold of the error encoding scheme isthree; as such the number of rows to divide the data partition into isthree. The number of columns for each row is set to 15, which is basedon the number and size of data blocks. The data blocks of the datapartition are arranged in rows and columns in a sequential order (i.e.,the first row includes the first 15 data blocks; the second row includesthe second 15 data blocks; and the third row includes the last 15 datablocks).

With the data blocks arranged into the desired sequential order, theyare divided into data segments based on the segmenting information. Inthis example, the data partition is divided into 8 data segments; thefirst 7 include 2 columns of three rows and the last includes 1 columnof three rows. Note that the first row of the 8 data segments is insequential order of the first 15 data blocks; the second row of the 8data segments in sequential order of the second 15 data blocks; and thethird row of the 8 data segments in sequential order of the last 15 datablocks. Note that the number of data blocks, the grouping of the datablocks into segments, and size of the data blocks may vary toaccommodate the desired distributed task processing function.

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding processing the data segmentsof FIG. 7. In this example, data segment 1 includes 3 rows with each rowbeing treated as one word for encoding. As such, data segment 1 includesthree words for encoding: word 1 including data blocks d1 and d2, word 2including data blocks d16 and d17, and word 3 including data blocks d31and d32. Each of data segments 2-7 includes three words where each wordincludes two data blocks. Data segment 8 includes three words where eachword includes a single data block (e.g., d15, d30, and d45).

In operation, an error encoding module 146 and a slicing module 148convert each data segment into a set of encoded data slices inaccordance with error correction encoding parameters as controlinformation 160. More specifically, when the error correction encodingparameters indicate a unity matrix Reed-Solomon based encodingalgorithm, 5 pillars, and decode threshold of 3, the first three encodeddata slices of the set of encoded data slices for a data segment aresubstantially similar to the corresponding word of the data segment. Forinstance, when the unity matrix Reed-Solomon based encoding algorithm isapplied to data segment 1, the content of the first encoded data slice(DS1_d1&2) of the first set of encoded data slices (e.g., correspondingto data segment 1) is substantially similar to content of the first word(e.g., d1 & d2); the content of the second encoded data slice(DS1_d16&17) of the first set of encoded data slices is substantiallysimilar to content of the second word (e.g., d16 & d17); and the contentof the third encoded data slice (DS1_d31&32) of the first set of encodeddata slices is substantially similar to content of the third word (e.g.,d31 & d32).

The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the first set of encoded data slices include error correctiondata based on the first-third words of the first data segment. With suchan encoding and slicing scheme, retrieving any three of the five encodeddata slices allows the data segment to be accurately reconstructed.

The encoding and slices of data segments 2-7 yield sets of encoded dataslices similar to the set of encoded data slices of data segment 1. Forinstance, the content of the first encoded data slice (DS2_d3&4) of thesecond set of encoded data slices (e.g., corresponding to data segment2) is substantially similar to content of the first word (e.g., d3 &d4); the content of the second encoded data slice (DS2_d18&19) of thesecond set of encoded data slices is substantially similar to content ofthe second word (e.g., d18 & d19); and the content of the third encodeddata slice (DS2_d33&34) of the second set of encoded data slices issubstantially similar to content of the third word (e.g., d33 & d34).The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the second set of encoded data slices includes errorcorrection data based on the first-third words of the second datasegment.

FIG. 9 is a diagram of an example of grouping selection processing of anoutbound distributed storage and task (DST) processing in accordancewith group selection information as control information 160 from acontrol module. Encoded slices for data partition 122 are grouped inaccordance with the control information 160 to produce slice groupings96. In this example, a grouping selection module 114 organizes theencoded data slices into five slice groupings (e.g., one for each DSTexecution unit of a distributed storage and task network (DSTN) module).As a specific example, the grouping selection module 114 creates a firstslice grouping for a DST execution unit #1, which includes first encodedslices of each of the sets of encoded slices. As such, the first DSTexecution unit receives encoded data slices corresponding to data blocks1-15 (e.g., encoded data slices of contiguous data).

The grouping selection module 114 also creates a second slice groupingfor a DST execution unit #2, which includes second encoded slices ofeach of the sets of encoded slices. As such, the second DST executionunit receives encoded data slices corresponding to data blocks 16-30.The grouping selection module 114 further creates a third slice groupingfor DST execution unit #3, which includes third encoded slices of eachof the sets of encoded slices. As such, the third DST execution unitreceives encoded data slices corresponding to data blocks 31-45.

The grouping selection module 114 creates a fourth slice grouping forDST execution unit #4, which includes fourth encoded slices of each ofthe sets of encoded slices. As such, the fourth DST execution unitreceives encoded data slices corresponding to first error encodinginformation (e.g., encoded data slices of error coding (EC) data). Thegrouping selection module 114 further creates a fifth slice grouping forDST execution unit #5, which includes fifth encoded slices of each ofthe sets of encoded slices. As such, the fifth DST execution unitreceives encoded data slices corresponding to second error encodinginformation.

FIG. 10 is a diagram of an example of converting data 92 into slicegroups that expands on the preceding figures. As shown, the data 92 ispartitioned in accordance with a partitioning function 164 into aplurality of data partitions (1-x, where x is an integer greater than4). Each data partition (or chunkset of data) is encoded and groupedinto slice groupings as previously discussed by an encoding and groupingfunction 166. For a given data partition, the slice groupings are sentto distributed storage and task (DST) execution units. From datapartition to data partition, the ordering of the slice groupings to theDST execution units may vary.

For example, the slice groupings of data partition #1 is sent to the DSTexecution units such that the first DST execution receives first encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a first continuous data chunk of the first data partition(e.g., refer to FIG. 9), a second DST execution receives second encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a second continuous data chunk of the first datapartition, etc.

For the second data partition, the slice groupings may be sent to theDST execution units in a different order than it was done for the firstdata partition. For instance, the first slice grouping of the seconddata partition (e.g., slice group 2_1) is sent to the second DSTexecution unit; the second slice grouping of the second data partition(e.g., slice group 2_2) is sent to the third DST execution unit; thethird slice grouping of the second data partition (e.g., slice group2_3) is sent to the fourth DST execution unit; the fourth slice groupingof the second data partition (e.g., slice group 2_4, which includesfirst error coding information) is sent to the fifth DST execution unit;and the fifth slice grouping of the second data partition (e.g., slicegroup 2_5, which includes second error coding information) is sent tothe first DST execution unit.

The pattern of sending the slice groupings to the set of DST executionunits may vary in a predicted pattern, a random pattern, and/or acombination thereof from data partition to data partition. In addition,from data partition to data partition, the set of DST execution unitsmay change. For example, for the first data partition, DST executionunits 1-5 may be used; for the second data partition, DST executionunits 6-10 may be used; for the third data partition, DST executionunits 3-7 may be used; etc. As is also shown, the task is divided intopartial tasks that are sent to the DST execution units in conjunctionwith the slice groupings of the data partitions.

FIG. 11 is a schematic block diagram of an embodiment of a DST(distributed storage and/or task) execution unit that includes aninterface 169, a controller 86, memory 88, one or more DT (distributedtask) execution modules 90, and a DST client module 34. The memory 88 isof sufficient size to store a significant number of encoded data slices(e.g., thousands of slices to hundreds-of-millions of slices) and mayinclude one or more hard drives and/or one or more solid-state memorydevices (e.g., flash memory, DRAM, etc.).

In an example of storing a slice group, the DST execution modulereceives a slice grouping 96 (e.g., slice group #1) via interface 169.The slice grouping 96 includes, per partition, encoded data slices ofcontiguous data or encoded data slices of error coding (EC) data. Forslice group #1, the DST execution module receives encoded data slices ofcontiguous data for partitions #1 and #x (and potentially others between3 and x) and receives encoded data slices of EC data for partitions #2and #3 (and potentially others between 3 and x). Examples of encodeddata slices of contiguous data and encoded data slices of error coding(EC) data are discussed with reference to FIG. 9. The memory 88 storesthe encoded data slices of slice groupings 96 in accordance with memorycontrol information 174 it receives from the controller 86.

The controller 86 (e.g., a processing module, a CPU, etc.) generates thememory control information 174 based on a partial task(s) 98 anddistributed computing information (e.g., user information (e.g., userID, distributed computing permissions, data access permission, etc.),vault information (e.g., virtual memory assigned to user, user group,temporary storage for task processing, etc.), task validationinformation, etc.). For example, the controller 86 interprets thepartial task(s) 98 in light of the distributed computing information todetermine whether a requestor is authorized to perform the task 98, isauthorized to access the data, and/or is authorized to perform the taskon this particular data. When the requestor is authorized, thecontroller 86 determines, based on the task 98 and/or another input,whether the encoded data slices of the slice grouping 96 are to betemporarily stored or permanently stored. Based on the foregoing, thecontroller 86 generates the memory control information 174 to write theencoded data slices of the slice grouping 96 into the memory 88 and toindicate whether the slice grouping 96 is permanently stored ortemporarily stored.

With the slice grouping 96 stored in the memory 88, the controller 86facilitates execution of the partial task(s) 98. In an example, thecontroller 86 interprets the partial task 98 in light of thecapabilities of the DT execution module(s) 90. The capabilities includeone or more of MIPS capabilities, processing resources (e.g., quantityand capability of microprocessors, CPUs, digital signal processors,co-processor, microcontrollers, arithmetic logic circuitry, and/or anyother analog and/or digital processing circuitry), availability of theprocessing resources, etc. If the controller 86 determines that the DTexecution module(s) 90 have sufficient capabilities, it generates taskcontrol information 176.

The task control information 176 may be a generic instruction (e.g.,perform the task on the stored slice grouping) or a series ofoperational codes. In the former instance, the DT execution module 90includes a co-processor function specifically configured (fixed orprogrammed) to perform the desired task 98. In the latter instance, theDT execution module 90 includes a general processor topology where thecontroller stores an algorithm corresponding to the particular task 98.In this instance, the controller 86 provides the operational codes(e.g., assembly language, source code of a programming language, objectcode, etc.) of the algorithm to the DT execution module 90 forexecution.

Depending on the nature of the task 98, the DT execution module 90 maygenerate intermediate partial results 102 that are stored in the memory88 or in a cache memory (not shown) within the DT execution module 90.In either case, when the DT execution module 90 completes execution ofthe partial task 98, it outputs one or more partial results 102. Thepartial results 102 may also be stored in memory 88.

If, when the controller 86 is interpreting whether capabilities of theDT execution module(s) 90 can support the partial task 98, thecontroller 86 determines that the DT execution module(s) 90 cannotadequately support the task 98 (e.g., does not have the right resources,does not have sufficient available resources, available resources wouldbe too slow, etc.), it then determines whether the partial task 98should be fully offloaded or partially offloaded.

If the controller 86 determines that the partial task 98 should be fullyoffloaded, it generates DST control information 178 and provides it tothe DST client module 34. The DST control information 178 includes thepartial task 98, memory storage information regarding the slice grouping96, and distribution instructions. The distribution instructionsinstruct the DST client module 34 to divide the partial task 98 intosub-partial tasks 172, to divide the slice grouping 96 into sub-slicegroupings 170, and identify other DST execution units. The DST clientmodule 34 functions in a similar manner as the DST client module 34 ofFIGS. 3-10 to produce the sub-partial tasks 172 and the sub-slicegroupings 170 in accordance with the distribution instructions.

The DST client module 34 receives DST feedback 168 (e.g., sub-partialresults), via the interface 169, from the DST execution units to whichthe task was offloaded. The DST client module 34 provides thesub-partial results to the DST execution unit, which processes thesub-partial results to produce the partial result(s) 102.

If the controller 86 determines that the partial task 98 should bepartially offloaded, it determines what portion of the task 98 and/orslice grouping 96 should be processed locally and what should beoffloaded. For the portion that is being locally processed, thecontroller 86 generates task control information 176 as previouslydiscussed. For the portion that is being offloaded, the controller 86generates DST control information 178 as previously discussed.

When the DST client module 34 receives DST feedback 168 (e.g.,sub-partial results) from the DST executions units to which a portion ofthe task was offloaded, it provides the sub-partial results to the DTexecution module 90. The DT execution module 90 processes thesub-partial results with the sub-partial results it created to producethe partial result(s) 102.

The memory 88 may be further utilized to retrieve one or more of storedslices 100, stored results 104, partial results 102 when the DTexecution module 90 stores partial results 102 and/or results 104 andthe memory 88. For example, when the partial task 98 includes aretrieval request, the controller 86 outputs the memory control 174 tothe memory 88 to facilitate retrieval of slices 100 and/or results 104.

FIG. 12 is a schematic block diagram of an example of operation of adistributed storage and task (DST) execution unit storing encoded dataslices and executing a task thereon. To store the encoded data slices ofa partition 1 of slice grouping 1, a controller 86 generates writecommands as memory control information 174 such that the encoded slicesare stored in desired locations (e.g., permanent or temporary) withinmemory 88.

Once the encoded slices are stored, the controller 86 provides taskcontrol information 176 to a distributed task (DT) execution module 90.As a first step of executing the task in accordance with the taskcontrol information 176, the DT execution module 90 retrieves theencoded slices from memory 88. The DT execution module 90 thenreconstructs contiguous data blocks of a data partition. As shown forthis example, reconstructed contiguous data blocks of data partition 1include data blocks 1-15 (e.g., d1-d15).

With the contiguous data blocks reconstructed, the DT execution module90 performs the task on the reconstructed contiguous data blocks. Forexample, the task may be to search the reconstructed contiguous datablocks for a particular word or phrase, identify where in thereconstructed contiguous data blocks the particular word or phraseoccurred, and/or count the occurrences of the particular word or phraseon the reconstructed contiguous data blocks. The DST execution unitcontinues in a similar manner for the encoded data slices of otherpartitions in slice grouping 1. Note that with using the unity matrixerror encoding scheme previously discussed, if the encoded data slicesof contiguous data are uncorrupted, the decoding of them is a relativelystraightforward process of extracting the data.

If, however, an encoded data slice of contiguous data is corrupted (ormissing), it can be rebuilt by accessing other DST execution units thatare storing the other encoded data slices of the set of encoded dataslices of the corrupted encoded data slice. In this instance, the DSTexecution unit having the corrupted encoded data slices retrieves atleast three encoded data slices (of contiguous data and of error codingdata) in the set from the other DST execution units (recall for thisexample, the pillar width is 5 and the decode threshold is 3). The DSTexecution unit decodes the retrieved data slices using the DS errorencoding parameters to recapture the corresponding data segment. The DSTexecution unit then re-encodes the data segment using the DS errorencoding parameters to rebuild the corrupted encoded data slice. Oncethe encoded data slice is rebuilt, the DST execution unit functions aspreviously described.

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing section 82 of a DSTclient module coupled to DST execution units of a distributed storageand task network (DSTN) module via a network 24. The inbound DSTprocessing section 82 includes a de-grouping module 180, a DS (dispersedstorage) error decoding module 182, a data de-partitioning module 184, acontrol module 186, and a distributed task control module 188. Note thatthe control module 186 and/or the distributed task control module 188may be separate modules from corresponding ones of outbound DSTprocessing section or may be the same modules.

In an example of operation, the DST execution units have completedexecution of corresponding partial tasks on the corresponding slicegroupings to produce partial results 102. The inbound DST processingsection 82 receives the partial results 102 via the distributed taskcontrol module 188. The inbound DST processing section 82 then processesthe partial results 102 to produce a final result, or results 104. Forexample, if the task was to find a specific word or phrase within data,the partial results 102 indicate where in each of the prescribedportions of the data the corresponding DST execution units found thespecific word or phrase. The distributed task control module 188combines the individual partial results 102 for the correspondingportions of the data into a final result 104 for the data as a whole.

In another example of operation, the inbound DST processing section 82is retrieving stored data from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices 100 corresponding to the data retrieval requests. The de-groupingmodule 180 receives retrieved slices 100 and de-groups them to produceencoded data slices per data partition 122. The DS error decoding module182 decodes, in accordance with DS error encoding parameters, theencoded data slices per data partition 122 to produce data partitions120.

The data de-partitioning module 184 combines the data partitions 120into the data 92. The control module 186 controls the conversion ofretrieve slices 100 into the data 92 using control signals 190 to eachof the modules. For instance, the control module 186 providesde-grouping information to the de-grouping module 180, provides the DSerror encoding parameters to the DS error decoding module 182, andprovides de-partitioning information to the data de-partitioning module184.

FIG. 14 is a logic diagram of an example of a method that is executableby distributed storage and task (DST) client module regarding inboundDST processing. The method begins at step 194 where the DST clientmodule receives partial results. The method continues at step 196 wherethe DST client module retrieves the task corresponding to the partialresults. For example, the partial results include header informationthat identifies the requesting entity, which correlates to the requestedtask.

The method continues at step 198 where the DST client module determinesresult processing information based on the task. For example, if thetask were to identify a particular word or phrase within the data, theresult processing information would indicate to aggregate the partialresults for the corresponding portions of the data to produce the finalresult. As another example, if the task were to count the occurrences ofa particular word or phrase within the data, results of processing theinformation would indicate to add the partial results to produce thefinal results. The method continues at step 200 where the DST clientmodule processes the partial results in accordance with the resultprocessing information to produce the final result or results.

FIG. 15 is a diagram of an example of de-grouping selection processingof an inbound distributed storage and task (DST) processing section of aDST client module. In general, this is an inverse process of thegrouping module of the outbound DST processing section of FIG. 9.Accordingly, for each data partition (e.g., partition #1), thede-grouping module retrieves the corresponding slice grouping from theDST execution units (EU) (e.g., DST 1-5).

As shown, DST execution unit #1 provides a first slice grouping, whichincludes the first encoded slices of each of the sets of encoded slices(e.g., encoded data slices of contiguous data of data blocks 1-15); DSTexecution unit #2 provides a second slice grouping, which includes thesecond encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 16-30); DSTexecution unit #3 provides a third slice grouping, which includes thethird encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 31-45); DSTexecution unit #4 provides a fourth slice grouping, which includes thefourth encoded slices of each of the sets of encoded slices (e.g., firstencoded data slices of error coding (EC) data); and DST execution unit#5 provides a fifth slice grouping, which includes the fifth encodedslices of each of the sets of encoded slices (e.g., first encoded dataslices of error coding (EC) data).

The de-grouping module de-groups the slice groupings (e.g., receivedslices 100) using a de-grouping selector 180 controlled by a controlsignal 190 as shown in the example to produce a plurality of sets ofencoded data slices (e.g., retrieved slices for a partition into sets ofslices 122). Each set corresponding to a data segment of the datapartition.

FIG. 16 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, a de-segmenting processing module 210, and acontrol module 186.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186, unsecures eachencoded data slice 122 based on slice de-security information receivedas control information 190 (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received from thecontrol module 186. The slice security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRCverification, etc.), and/or any other type of digital security. Forexample, when the inverse per slice security processing module 202 isenabled, it verifies integrity information (e.g., a CRC value) of eachencoded data slice 122, it decrypts each verified encoded data slice,and decompresses each decrypted encoded data slice to produce sliceencoded data 158. When the inverse per slice security processing module202 is not enabled, it passes the encoded data slices 122 as the slicedencoded data 158 or is bypassed such that the retrieved encoded dataslices 122 are provided as the sliced encoded data 158.

The de-slicing module 204 de-slices the sliced encoded data 158 intoencoded data segments 156 in accordance with a pillar width of the errorcorrection encoding parameters received as control information 190 fromthe control module 186. For example, if the pillar width is five, thede-slicing module 204 de-slices a set of five encoded data slices intoan encoded data segment 156. The error decoding module 206 decodes theencoded data segments 156 in accordance with error correction decodingparameters received as control information 190 from the control module186 to produce secure data segments 154. The error correction decodingparameters include identifying an error correction encoding scheme(e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction decoding parameters identify a specific errorcorrection encoding scheme, specify a pillar width of five, and specifya decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments 154 based onsegment security information received as control information 190 fromthe control module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module 208 isenabled, it verifies integrity information (e.g., a CRC value) of eachsecure data segment 154, it decrypts each verified secured data segment,and decompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 154 as the data segment152 or is bypassed.

The de-segment processing module 210 receives the data segments 152 andreceives de-segmenting information as control information 190 from thecontrol module 186. The de-segmenting information indicates how thede-segment processing module 210 is to de-segment the data segments 152into a data partition 120. For example, the de-segmenting informationindicates how the rows and columns of data segments are to be rearrangedto yield the data partition 120.

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of a dispersed error decoding module. A de-slicing module 204receives at least a decode threshold number of encoded data slices 158for each data segment in accordance with control information 190 andprovides encoded data 156. In this example, a decode threshold is three.As such, each set of encoded data slices 158 is shown to have threeencoded data slices per data segment. The de-slicing module 204 mayreceive three encoded data slices per data segment because an associateddistributed storage and task (DST) client module requested retrievingonly three encoded data slices per segment or selected three of theretrieved encoded data slices per data segment. As shown, which is basedon the unity matrix encoding previously discussed with reference to FIG.8, an encoded data slice may be a data-based encoded data slice (e.g.,DS1_d1&d2) or an error code based encoded data slice (e.g., ES3_1).

An error decoding module 206 decodes the encoded data 156 of each datasegment in accordance with the error correction decoding parameters ofcontrol information 190 to produce secured segments 154. In thisexample, data segment 1 includes 3 rows with each row being treated asone word for encoding. As such, data segment 1 includes three words:word 1 including data blocks d1 and d2, word 2 including data blocks d16and d17, and word 3 including data blocks d31 and d32. Each of datasegments 2-7 includes three words where each word includes two datablocks. Data segment 8 includes three words where each word includes asingle data block (e.g., d15, d30, and d45).

FIG. 18 is a diagram of an example of de-segment processing of aninbound distributed storage and task (DST) processing. In this example,a de-segment processing module 210 receives data segments 152 (e.g.,1-8) and rearranges the data blocks of the data segments into rows andcolumns in accordance with de-segmenting information of controlinformation 190 to produce a data partition 120. Note that the number ofrows is based on the decode threshold (e.g., 3 in this specific example)and the number of columns is based on the number and size of the datablocks.

The de-segmenting module 210 converts the rows and columns of datablocks into the data partition 120. Note that each data block may be ofthe same size as other data blocks or of a different size. In addition,the size of each data block may be a few bytes to megabytes of data.

FIG. 19 is a diagram of an example of converting slice groups into data92 within an inbound distributed storage and task (DST) processingsection. As shown, the data 92 is reconstructed from a plurality of datapartitions (1-x, where x is an integer greater than 4). Each datapartition (or chunk set of data) is decoded and re-grouped using ade-grouping and decoding function 212 and a de-partition function 214from slice groupings as previously discussed. For a given datapartition, the slice groupings (e.g., at least a decode threshold perdata segment of encoded data slices) are received from DST executionunits. From data partition to data partition, the ordering of the slicegroupings received from the DST execution units may vary as discussedwith reference to FIG. 10.

FIG. 20 is a diagram of an example of a distributed storage and/orretrieval within the distributed computing system. The distributedcomputing system includes a plurality of distributed storage and/or task(DST) processing client modules 34 (one shown) coupled to a distributedstorage and/or task processing network (DSTN) module, or multiple DSTNmodules, via a network 24. The DST client module 34 includes an outboundDST processing section 80 and an inbound DST processing section 82. TheDSTN module includes a plurality of DST execution units. Each DSTexecution unit includes a controller 86, memory 88, one or moredistributed task (DT) execution modules 90, and a DST client module 34.

In an example of data storage, the DST client module 34 has data 92 thatit desires to store in the DSTN module. The data 92 may be a file (e.g.,video, audio, text, graphics, etc.), a data object, a data block, anupdate to a file, an update to a data block, etc. In this instance, theoutbound DST processing module 80 converts the data 92 into encoded dataslices 216 as will be further described with reference to FIGS. 21-23.The outbound DST processing module 80 sends, via the network 24, to theDST execution units for storage as further described with reference toFIG. 24.

In an example of data retrieval, the DST client module 34 issues aretrieve request to the DST execution units for the desired data 92. Theretrieve request may address each DST executions units storing encodeddata slices of the desired data, address a decode threshold number ofDST execution units, address a read threshold number of DST executionunits, or address some other number of DST execution units. In responseto the request, each addressed DST execution unit retrieves its encodeddata slices 100 of the desired data and sends them to the inbound DSTprocessing section 82, via the network 24.

When, for each data segment, the inbound DST processing section 82receives at least a decode threshold number of encoded data slices 100,it converts the encoded data slices 100 into a data segment. The inboundDST processing section 82 aggregates the data segments to produce theretrieved data 92.

FIG. 21 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module coupled to a distributed storage and task network (DSTN)module (e.g., a plurality of DST execution units) via a network 24. Theoutbound DST processing section 80 includes a data partitioning module110, a dispersed storage (DS) error encoding module 112, a groupingselector module 114, a control module 116, and a distributed taskcontrol module 118.

In an example of operation, the data partitioning module 110 isby-passed such that data 92 is provided directly to the DS errorencoding module 112. The control module 116 coordinates the by-passingof the data partitioning module 110 by outputting a bypass 220 messageto the data partitioning module 110.

The DS error encoding module 112 receives the data 92 in a serialmanner, a parallel manner, and/or a combination thereof. The DS errorencoding module 112 DS error encodes the data in accordance with controlinformation 160 from the control module 116 to produce encoded dataslices 218. The DS error encoding includes segmenting the data 92 intodata segments, segment security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC, etc.)), errorencoding, slicing, and/or per slice security processing (e.g.,encryption, compression, watermarking, integrity check (e.g., CRC,etc.)). The control information 160 indicates which steps of the DSerror encoding are active for the data 92 and, for active steps,indicates the parameters for the step. For example, the controlinformation 160 indicates that the error encoding is active and includeserror encoding parameters (e.g., pillar width, decode threshold, writethreshold, read threshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 218 of thedata segments into pillars of slices 216. The number of pillarscorresponds to the pillar width of the DS error encoding parameters. Inthis example, the distributed task control module 118 facilitates thestorage request.

FIG. 22 is a schematic block diagram of an example of a dispersedstorage (DS) error encoding module 112 for the example of FIG. 21. TheDS error encoding module 112 includes a segment processing module 142, asegment security processing module 144, an error encoding module 146, aslicing module 148, and a per slice security processing module 150. Eachof these modules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receivesdata 92 and receives segmenting information as control information 160from the control module 116. The segmenting information indicates howthe segment processing module is to segment the data. For example, thesegmenting information indicates the size of each data segment. Thesegment processing module 142 segments the data 92 into data segments152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., CRC, etc.), and/or anyother type of digital security. For example, when the segment securityprocessing module 144 is enabled, it compresses a data segment 152,encrypts the compressed data segment, and generates a CRC value for theencrypted data segment to produce a secure data segment. When thesegment security processing module 144 is not enabled, it passes thedata segments 152 to the error encoding module 146 or is bypassed suchthat the data segments 152 are provided to the error encoding module146.

The error encoding module 146 encodes the secure data segments inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction encoding parameters identify a specific errorcorrection encoding scheme, specifies a pillar width of five, andspecifies a decode threshold of three. From these parameters, the errorencoding module 146 encodes a data segment to produce an encoded datasegment.

The slicing module 148 slices the encoded data segment in accordancewith a pillar width of the error correction encoding parameters. Forexample, if the pillar width is five, the slicing module slices anencoded data segment into a set of five encoded data slices. As such,for a plurality of data segments, the slicing module 148 outputs aplurality of sets of encoded data slices as shown within encoding andslicing function 222 as described.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it may compress anencoded data slice, encrypt the compressed encoded data slice, andgenerate a CRC value for the encrypted encoded data slice to produce asecure encoded data slice tweaking. When the per slice securityprocessing module 150 is not enabled, it passes the encoded data slicesor is bypassed such that the encoded data slices 218 are the output ofthe DS error encoding module 112.

FIG. 23 is a diagram of an example of converting data 92 into pillarslice groups utilizing encoding, slicing and pillar grouping function224 for storage in memory of a distributed storage and task network(DSTN) module. As previously discussed the data 92 is encoded and slicedinto a plurality of sets of encoded data slices; one set per datasegment. The grouping selection module organizes the sets of encodeddata slices into pillars of data slices. In this example, the DS errorencoding parameters include a pillar width of 5 and a decode thresholdof 3. As such, for each data segment, 5 encoded data slices are created.

The grouping selection module takes the first encoded data slice of eachof the sets and forms a first pillar, which may be sent to the first DSTexecution unit. Similarly, the grouping selection module creates thesecond pillar from the second slices of the sets; the third pillar fromthe third slices of the sets; the fourth pillar from the fourth slicesof the sets; and the fifth pillar from the fifth slices of the set.

FIG. 24 is a schematic block diagram of an embodiment of a distributedstorage and/or task (DST) execution unit that includes an interface 169,a controller 86, memory 88, one or more distributed task (DT) executionmodules 90, and a DST client module 34. A computing core 26 may beutilized to implement the one or more DT execution modules 90 and theDST client module 34. The memory 88 is of sufficient size to store asignificant number of encoded data slices (e.g., thousands of slices tohundreds-of-millions of slices) and may include one or more hard drivesand/or one or more solid-state memory devices (e.g., flash memory, DRAM,etc.).

In an example of storing a pillar of slices 216, the DST execution unitreceives, via interface 169, a pillar of slices 216 (e.g., pillar #1slices). The memory 88 stores the encoded data slices 216 of the pillarof slices in accordance with memory control information 174 it receivesfrom the controller 86. The controller 86 (e.g., a processing module, aCPU, etc.) generates the memory control information 174 based ondistributed storage information (e.g., user information (e.g., user ID,distributed storage permissions, data access permission, etc.), vaultinformation (e.g., virtual memory assigned to user, user group, etc.),etc.). Similarly, when retrieving slices, the DST execution unitreceives, via interface 169, a slice retrieval request. The memory 88retrieves the slice in accordance with memory control information 174 itreceives from the controller 86. The memory 88 outputs the slice 100,via the interface 169, to a requesting entity.

FIG. 25 is a schematic block diagram of an example of operation of aninbound distributed storage and/or task (DST) processing section 82 forretrieving dispersed error encoded data 92. The inbound DST processingsection 82 includes a de-grouping module 180, a dispersed storage (DS)error decoding module 182, a data de-partitioning module 184, a controlmodule 186, and a distributed task control module 188. Note that thecontrol module 186 and/or the distributed task control module 188 may beseparate modules from corresponding ones of an outbound DST processingsection or may be the same modules.

In an example of operation, the inbound DST processing section 82 isretrieving stored data 92 from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices corresponding to data retrieval requests from the distributedtask control module 188. The de-grouping module 180 receives pillars ofslices 100 and de-groups them in accordance with control information 190from the control module 186 to produce sets of encoded data slices 218.The DS error decoding module 182 decodes, in accordance with the DSerror encoding parameters received as control information 190 from thecontrol module 186, each set of encoded data slices 218 to produce datasegments, which are aggregated into retrieved data 92. The datade-partitioning module 184 is by-passed in this operational mode via abypass signal 226 of control information 190 from the control module186.

FIG. 26 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, and a de-segmenting processing module 210. Thedispersed error decoding module 182 is operable to de-slice and decodeencoded slices per data segment 218 utilizing a de-slicing and decodingfunction 228 to produce a plurality of data segments that arede-segmented utilizing a de-segment function 230 to recover data 92.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186 via controlinformation 190, unsecures each encoded data slice 218 based on slicede-security information (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received as controlinformation 190 from the control module 186. The slice de-securityinformation includes data decompression, decryption, de-watermarking,integrity check (e.g., CRC verification, etc.), and/or any other type ofdigital security. For example, when the inverse per slice securityprocessing module 202 is enabled, it verifies integrity information(e.g., a CRC value) of each encoded data slice 218, it decrypts eachverified encoded data slice, and decompresses each decrypted encodeddata slice to produce slice encoded data. When the inverse per slicesecurity processing module 202 is not enabled, it passes the encodeddata slices 218 as the sliced encoded data or is bypassed such that theretrieved encoded data slices 218 are provided as the sliced encodeddata.

The de-slicing module 204 de-slices the sliced encoded data into encodeddata segments in accordance with a pillar width of the error correctionencoding parameters received as control information 190 from a controlmodule 186. For example, if the pillar width is five, the de-slicingmodule de-slices a set of five encoded data slices into an encoded datasegment. Alternatively, the encoded data segment may include just threeencoded data slices (e.g., when the decode threshold is 3).

The error decoding module 206 decodes the encoded data segments inaccordance with error correction decoding parameters received as controlinformation 190 from the control module 186 to produce secure datasegments. The error correction decoding parameters include identifyingan error correction encoding scheme (e.g., forward error correctionalgorithm, a Reed-Solomon based algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction decodingparameters identify a specific error correction encoding scheme, specifya pillar width of five, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments based on segmentsecurity information received as control information 190 from thecontrol module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module is enabled,it verifies integrity information (e.g., a CRC value) of each securedata segment, it decrypts each verified secured data segment, anddecompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 152 as the data segmentor is bypassed. The de-segmenting processing module 210 aggregates thedata segments 152 into the data 92 in accordance with controlinformation 190 from the control module 186.

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module that includes aplurality of distributed storage and task (DST) execution units (#1through #n, where, for example, n is an integer greater than or equal tothree). Each of the DST execution units includes a DST client module 34,a controller 86, one or more DT (distributed task) execution modules 90,and memory 88.

In this example, the DSTN module stores, in the memory of the DSTexecution units, a plurality of DS (dispersed storage) encoded data(e.g., 1 through n, where n is an integer greater than or equal to two)and stores a plurality of DS encoded task codes (e.g., 1 through k,where k is an integer greater than or equal to two). The DS encoded datamay be encoded in accordance with one or more examples described withreference to FIGS. 3-19 (e.g., organized in slice groupings) or encodedin accordance with one or more examples described with reference toFIGS. 20-26 (e.g., organized in pillar groups). The data that is encodedinto the DS encoded data may be of any size and/or of any content. Forexample, the data may be one or more digital books, a copy of acompany's emails, a large-scale Internet search, a video security file,one or more entertainment video files (e.g., television programs,movies, etc.), data files, and/or any other large amount of data (e.g.,greater than a few Terabytes).

The tasks that are encoded into the DS encoded task code may be a simplefunction (e.g., a mathematical function, a logic function, an identifyfunction, a find function, a search engine function, a replace function,etc.), a complex function (e.g., compression, human and/or computerlanguage translation, text-to-voice conversion, voice-to-textconversion, etc.), multiple simple and/or complex functions, one or morealgorithms, one or more applications, etc. The tasks may be encoded intothe DS encoded task code in accordance with one or more examplesdescribed with reference to FIGS. 3-19 (e.g., organized in slicegroupings) or encoded in accordance with one or more examples describedwith reference to FIGS. 20-26 (e.g., organized in pillar groups).

In an example of operation, a DST client module of a user device or of aDST processing unit issues a DST request to the DSTN module. The DSTrequest may include a request to retrieve stored data, or a portionthereof, may include a request to store data that is included with theDST request, may include a request to perform one or more tasks onstored data, may include a request to perform one or more tasks on dataincluded with the DST request, etc. In the cases where the DST requestincludes a request to store data or to retrieve data, the client moduleand/or the DSTN module processes the request as previously discussedwith reference to one or more of FIGS. 3-19 (e.g., slice groupings)and/or 20-26 (e.g., pillar groupings). In the case where the DST requestincludes a request to perform one or more tasks on data included withthe DST request, the DST client module and/or the DSTN module processthe DST request as previously discussed with reference to one or more ofFIGS. 3-19.

In the case where the DST request includes a request to perform one ormore tasks on stored data, the DST client module and/or the DSTN moduleprocesses the DST request as will be described with reference to one ormore of FIGS. 28-39. In general, the DST client module identifies dataand one or more tasks for the DSTN module to execute upon the identifieddata. The DST request may be for a one-time execution of the task or foran on-going execution of the task. As an example of the latter, as acompany generates daily emails, the DST request may be to daily searchnew emails for inappropriate content and, if found, record the content,the email sender(s), the email recipient(s), email routing information,notify human resources of the identified email, etc.

FIG. 28 is a schematic block diagram of an example of a distributedcomputing system performing tasks on stored data. In this example, twodistributed storage and task (DST) client modules 1-2 are shown: thefirst may be associated with a user device and the second may beassociated with a DST processing unit or a high priority user device(e.g., high priority clearance user, system administrator, etc.). EachDST client module includes a list of stored data 234 and a list of taskscodes 236. The list of stored data 234 includes one or more entries ofdata identifying information, where each entry identifies data stored inthe DSTN module 22. The data identifying information (e.g., data ID)includes one or more of a data file name, a data file directory listing,DSTN addressing information of the data, a data object identifier, etc.The list of tasks 236 includes one or more entries of task codeidentifying information, when each entry identifies task codes stored inthe DSTN module 22. The task code identifying information (e.g., taskID) includes one or more of a task file name, a task file directorylisting, DSTN addressing information of the task, another type ofidentifier to identify the task, etc.

As shown, the list of data 234 and the list of tasks 236 are eachsmaller in number of entries for the first DST client module than thecorresponding lists of the second DST client module. This may occurbecause the user device associated with the first DST client module hasfewer privileges in the distributed computing system than the deviceassociated with the second DST client module. Alternatively, this mayoccur because the user device associated with the first DST clientmodule serves fewer users than the device associated with the second DSTclient module and is restricted by the distributed computing systemaccordingly. As yet another alternative, this may occur through norestraints by the distributed computing system, it just occurred becausethe operator of the user device associated with the first DST clientmodule has selected fewer data and/or fewer tasks than the operator ofthe device associated with the second DST client module.

In an example of operation, the first DST client module selects one ormore data entries 238 and one or more tasks 240 from its respectivelists (e.g., selected data ID and selected task ID). The first DSTclient module sends its selections to a task distribution module 232.The task distribution module 232 may be within a stand-alone device ofthe distributed computing system, may be within the user device thatcontains the first DST client module, or may be within the DSTN module22.

Regardless of the task distribution module's location, it generates DSTallocation information 242 from the selected task ID 240 and theselected data ID 238. The DST allocation information 242 includes datapartitioning information, task execution information, and/orintermediate result information. The task distribution module 232 sendsthe DST allocation information 242 to the DSTN module 22. Note that oneor more examples of the DST allocation information will be discussedwith reference to one or more of FIGS. 29-39.

The DSTN module 22 interprets the DST allocation information 242 toidentify the stored DS encoded data (e.g., DS error encoded data 2) andto identify the stored DS error encoded task code (e.g., DS errorencoded task code 1). In addition, the DSTN module 22 interprets the DSTallocation information 242 to determine how the data is to bepartitioned and how the task is to be partitioned. The DSTN module 22also determines whether the selected DS error encoded data 238 needs tobe converted from pillar grouping to slice grouping. If so, the DSTNmodule 22 converts the selected DS error encoded data into slicegroupings and stores the slice grouping DS error encoded data byoverwriting the pillar grouping DS error encoded data or by storing itin a different location in the memory of the DSTN module 22 (i.e., doesnot overwrite the pillar grouping DS encoded data).

The DSTN module 22 partitions the data and the task as indicated in theDST allocation information 242 and sends the portions to selected DSTexecution units of the DSTN module 22. Each of the selected DSTexecution units performs its partial task(s) on its slice groupings toproduce partial results. The DSTN module 22 collects the partial resultsfrom the selected DST execution units and provides them, as resultinformation 244, to the task distribution module. The result information244 may be the collected partial results, one or more final results asproduced by the DSTN module 22 from processing the partial results inaccordance with the DST allocation information 242, or one or moreintermediate results as produced by the DSTN module 22 from processingthe partial results in accordance with the DST allocation information242.

The task distribution module 232 receives the result information 244 andprovides one or more final results 104 therefrom to the first DST clientmodule. The final result(s) 104 may be result information 244 or aresult(s) of the task distribution module's processing of the resultinformation 244.

In concurrence with processing the selected task of the first DST clientmodule, the distributed computing system may process the selectedtask(s) of the second DST client module on the selected data(s) of thesecond DST client module. Alternatively, the distributed computingsystem may process the second DST client module's request subsequent to,or preceding, that of the first DST client module. Regardless of theordering and/or parallel processing of the DST client module requests,the second DST client module provides its selected data 238 and selectedtask 240 to a task distribution module 232. If the task distributionmodule 232 is a separate device of the distributed computing system orwithin the DSTN module, the task distribution modules 232 coupled to thefirst and second DST client modules may be the same module. The taskdistribution module 232 processes the request of the second DST clientmodule in a similar manner as it processed the request of the first DSTclient module.

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module 232 facilitating the example of FIG. 28. The taskdistribution module 232 includes a plurality of tables it uses togenerate distributed storage and task (DST) allocation information 242for selected data and selected tasks received from a DST client module.The tables include data storage information 248, task storageinformation 250, distributed task (DT) execution module information 252,and task

sub-task mapping information 246.

The data storage information table 248 includes a data identification(ID) field 260, a data size field 262, an addressing information field264, distributed storage (DS) information 266, and may further includeother information regarding the data, how it is stored, and/or how itcan be processed. For example, DS encoded data #1 has a data ID of 1, adata size of AA (e.g., a byte size of a few Terabytes or more),addressing information of Addr_1_AA, and DS parameters of 3/5; SEG_1;and SLC_1. In this example, the addressing information may be a virtualaddress corresponding to the virtual address of the first storage word(e.g., one or more bytes) of the data and information on how tocalculate the other addresses, may be a range of virtual addresses forthe storage words of the data, physical addresses of the first storageword or the storage words of the data, may be a list of slice names ofthe encoded data slices of the data, etc. The DS parameters may includeidentity of an error encoding scheme, decode threshold/pillar width(e.g., 3/5 for the first data entry), segment security information(e.g., SEG_1), per slice security information (e.g., SLC_1), and/or anyother information regarding how the data was encoded into data slices.

The task storage information table 250 includes a task identification(ID) field 268, a task size field 270, an addressing information field272, distributed storage (DS) information 274, and may further includeother information regarding the task, how it is stored, and/or how itcan be used to process data. For example, DS encoded task #2 has a taskID of 2, a task size of XY, addressing information of Addr_2_XY, and DSparameters of 3/5; SEG_2; and SLC_2. In this example, the addressinginformation may be a virtual address corresponding to the virtualaddress of the first storage word (e.g., one or more bytes) of the taskand information on how to calculate the other addresses, may be a rangeof virtual addresses for the storage words of the task, physicaladdresses of the first storage word or the storage words of the task,may be a list of slices names of the encoded slices of the task code,etc. The DS parameters may include identity of an error encoding scheme,decode threshold/pillar width (e.g., 3/5 for the first data entry),segment security information (e.g., SEG_2), per slice securityinformation (e.g., SLC_2), and/or any other information regarding howthe task was encoded into encoded task slices. Note that the segmentand/or the per-slice security information include a type of encryption(if enabled), a type of compression (if enabled), watermarkinginformation (if enabled), and/or an integrity check scheme (if enabled).

The task

sub-task mapping information table 246 includes a task field 256 and asub-task field 258. The task field 256 identifies a task stored in thememory of a distributed storage and task network (DSTN) module and thecorresponding sub-task fields 258 indicates whether the task includessub-tasks and, if so, how many and if any of the sub-tasks are ordered.In this example, the task

sub-task mapping information table 246 includes an entry for each taskstored in memory of the DSTN module (e.g., task 1 through task k). Inparticular, this example indicates that task 1 includes 7 sub-tasks;task 2 does not include sub-tasks, and task k includes r number ofsub-tasks (where r is an integer greater than or equal to two).

The DT execution module table 252 includes a DST execution unit ID field276, a DT execution module ID field 278, and a DT execution modulecapabilities field 280. The DST execution unit ID field 276 includes theidentity of DST units in the DSTN module. The DT execution module IDfield 278 includes the identity of each DT execution unit in each DSTunit. For example, DST unit 1 includes three DT executions modules(e.g., 1_1, 1_2, and 1_3). The DT execution capabilities field 280includes identity of the capabilities of the corresponding DT executionunit. For example, DT execution module 1_1 includes capabilities X,where X includes one or more of MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or any other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.), and/or any information germane toexecuting one or more tasks.

From these tables, the task distribution module 232 generates the DSTallocation information 242 to indicate where the data is stored, how topartition the data, where the task is stored, how to partition the task,which DT execution units should perform which partial task on which datapartitions, where and how intermediate results are to be stored, etc. Ifmultiple tasks are being performed on the same data or different data,the task distribution module factors such information into itsgeneration of the DST allocation information.

FIG. 30 is a diagram of a specific example of a distributed computingsystem performing tasks on stored data as a task flow 318. In thisexample, selected data 92 is data 2 and selected tasks are tasks 1, 2,and 3. Task 1 corresponds to analyzing translation of data from onelanguage to another (e.g., human language or computer language); task 2corresponds to finding specific words and/or phrases in the data; andtask 3 corresponds to finding specific translated words and/or phrasesin translated data.

In this example, task 1 includes 7 sub-tasks: task 1_1—identifynon-words (non-ordered); task 1_2—identify unique words (non-ordered);task 1_3—translate (non-ordered); task 1_4—translate back (ordered aftertask 1_3); task 1_5—compare to ID errors (ordered after task 1-4); task1_6—determine non-word translation errors (ordered after task 1_5 and1_1); and task 1_7—determine correct translations (ordered after 1_5 and1_2). The sub-task further indicates whether they are an ordered task(i.e., are dependent on the outcome of another task) or non-order (i.e.,are independent of the outcome of another task). Task 2 does not includesub-tasks and task 3 includes two sub-tasks: task 3_1 translate; andtask 3_2 find specific word or phrase in translated data.

In general, the three tasks collectively are selected to analyze datafor translation accuracies, translation errors, translation anomalies,occurrence of specific words or phrases in the data, and occurrence ofspecific words or phrases on the translated data. Graphically, the data92 is translated 306 into translated data 282; is analyzed for specificwords and/or phrases 300 to produce a list of specific words and/orphrases 286; is analyzed for non-words 302 (e.g., not in a referencedictionary) to produce a list of non-words 290; and is analyzed forunique words 316 included in the data 92 (i.e., how many different wordsare included in the data) to produce a list of unique words 298. Each ofthese tasks is independent of each other and can therefore be processedin parallel if desired.

The translated data 282 is analyzed (e.g., sub-task 3_2) for specifictranslated words and/or phrases 304 to produce a list of specifictranslated words and/or phrases 288. The translated data 282 istranslated back 308 (e.g., sub-task 1_4) into the language of theoriginal data to produce re-translated data 284. These two tasks aredependent on the translate task (e.g., task 1_3) and thus must beordered after the translation task, which may be in a pipelined orderingor a serial ordering. The re-translated data 284 is then compared 310with the original data 92 to find words and/or phrases that did nottranslate (one way and/or the other) properly to produce a list ofincorrectly translated words 294. As such, the comparing task (e.g.,sub-task 1_5) 310 is ordered after the translation 306 andre-translation tasks 308 (e.g., sub-tasks 1_3 and 1_4).

The list of words incorrectly translated 294 is compared 312 to the listof non-words 290 to identify words that were not properly translatedbecause the words are non-words to produce a list of errors due tonon-words 292. In addition, the list of words incorrectly translated 294is compared 314 to the list of unique words 298 to identify unique wordsthat were properly translated to produce a list of correctly translatedwords 296. The comparison may also identify unique words that were notproperly translated to produce a list of unique words that were notproperly translated. Note that each list of words (e.g., specific wordsand/or phrases, non-words, unique words, translated words and/orphrases, etc.,) may include the word and/or phrase, how many times it isused, where in the data it is used, and/or any other informationrequested regarding a word and/or phrase.

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30. As shown, DS encoded data 2 is storedas encoded data slices across the memory (e.g., stored in memories 88)of DST execution units 1-5; the DS encoded task code 1 (of task 1) andDS encoded task 3 are stored as encoded task slices across the memory ofDST execution units 1-5; and DS encoded task code 2 (of task 2) isstored as encoded task slices across the memory of DST execution units3-7. As indicated in the data storage information table and the taskstorage information table of FIG. 29, the respective data/task has DSparameters of 3/5 for their decode threshold/pillar width; hencespanning the memory of five DST execution units.

FIG. 32 is a diagram of an example of distributed storage and task (DST)allocation information 242 for the example of FIG. 30. The DSTallocation information 242 includes data partitioning information 320,task execution information 322, and intermediate result information 324.The data partitioning information 320 includes the data identifier (ID),the number of partitions to split the data into, address information foreach data partition, and whether the DS encoded data has to betransformed from pillar grouping to slice grouping. The task executioninformation 322 includes tabular information having a taskidentification field 326, a task ordering field 328, a data partitionfield ID 330, and a set of DT execution modules 332 to use for thedistributed task processing per data partition. The intermediate resultinformation 324 includes tabular information having a name ID field 334,an ID of the DST execution unit assigned to process the correspondingintermediate result 336, a scratch pad storage field 338, and anintermediate result storage field 340.

Continuing with the example of FIG. 30, where tasks 1-3 are to bedistributedly performed on data 2, the data partitioning informationincludes the ID of data 2. In addition, the task distribution moduledetermines whether the DS encoded data 2 is in the proper format fordistributed computing (e.g., was stored as slice groupings). If not, thetask distribution module indicates that the DS encoded data 2 formatneeds to be changed from the pillar grouping format to the slicegrouping format, which will be done the by DSTN module. In addition, thetask distribution module determines the number of partitions to dividethe data into (e.g., 2_1 through 2_z) and addressing information foreach partition.

The task distribution module generates an entry in the task executioninformation section for each sub-task to be performed. For example, task1_1 (e.g., identify non-words on the data) has no task ordering (i.e.,is independent of the results of other sub-tasks), is to be performed ondata partitions 2_1 through 2_z by DT execution modules 1_1, 2_1, 3_1,4_1, and 5_1. For instance, DT execution modules 1_1, 2_1, 3_1, 4_1, and5_1 search for non-words in data partitions 2_1 through 2_z to producetask 1_1 intermediate results (R1-1, which is a list of non-words). Task1_2 (e.g., identify unique words) has similar task execution informationas task 1_1 to produce task 1_2 intermediate results (R1-2, which is thelist of unique words).

Task 1_3 (e.g., translate) includes task execution information as beingnon-ordered (i.e., is independent), having DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1 through 2_4 andhaving DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2 translate datapartitions 2_5 through 2_z to produce task 1_3 intermediate results(R1-3, which is the translated data). In this example, the datapartitions are grouped, where different sets of DT execution modulesperform a distributed sub-task (or task) on each data partition group,which allows for further parallel processing.

Task 1_4 (e.g., translate back) is ordered after task 1_3 and is to beexecuted on task 1_3's intermediate result (e.g., R1-3_1) (e.g., thetranslated data). DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 areallocated to translate back task 1_3 intermediate result partitionsR1-3_1 through R1-3_4 and DT execution modules 1_2, 2_2, 6_1, 7_1, and7_2 are allocated to translate back task 1_3 intermediate resultpartitions R1-3_5 through R1-3_z to produce task 1-4 intermediateresults (R1-4, which is the translated back data).

Task 1_5 (e.g., compare data and translated data to identify translationerrors) is ordered after task 1_4 and is to be executed on task 1_4'sintermediate results (R4-1) and on the data. DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 are allocated to compare the data partitions (2_1through 2_z) with partitions of task 1-4 intermediate results partitionsR1-4_1 through R1-4_z to produce task 1_5 intermediate results (R1-5,which is the list words translated incorrectly).

Task 1_6 (e.g., determine non-word translation errors) is ordered aftertasks 1_1 and 1_5 and is to be executed on tasks 1_1's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_1, 2_1,3_1, 4_1, and 5_1 are allocated to compare the partitions of task 1_1intermediate results (R1-1_1 through R1-1_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_6 intermediate results (R1-6, which is the list translation errors dueto non-words).

Task 1_7 (e.g., determine words correctly translated) is ordered aftertasks 1_2 and 1_5 and is to be executed on tasks 1_2's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_2, 2_2,3_2, 4_2, and 5_2 are allocated to compare the partitions of task 1_2intermediate results (R1-2_1 through R1-2_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_7 intermediate results (R1-7, which is the list of correctlytranslated words).

Task 2 (e.g., find specific words and/or phrases) has no task ordering(i.e., is independent of the results of other sub-tasks), is to beperformed on data partitions 2_1 through 2_z by DT execution modules3_1, 4_1, 5_1, 6_1, and 7_1. For instance, DT execution modules 3_1,4_1, 5_1, 6_1, and 7_1 search for specific words and/or phrases in datapartitions 2_1 through 2_z to produce task 2 intermediate results (R2,which is a list of specific words and/or phrases).

Task 3_2 (e.g., find specific translated words and/or phrases) isordered after task 1_3 (e.g., translate) is to be performed onpartitions R1-3_1 through R1-3_z by DT execution modules 1_2, 2_2, 3_2,4_2, and 5_2. For instance, DT execution modules 1_2, 2_2, 3_2, 4_2, and5_2 search for specific translated words and/or phrases in thepartitions of the translated data (R1-3_1 through R1-3_z) to producetask 3_2 intermediate results (R3-2, which is a list of specifictranslated words and/or phrases).

For each task, the intermediate result information indicates which DSTunit is responsible for overseeing execution of the task and, if needed,processing the partial results generated by the set of allocated DTexecution units. In addition, the intermediate result informationindicates a scratch pad memory for the task and where the correspondingintermediate results are to be stored. For example, for intermediateresult R1-1 (the intermediate result of task 1_1), DST unit 1 isresponsible for overseeing execution of the task 1_1 and coordinatesstorage of the intermediate result as encoded intermediate result slicesstored in memory of DST execution units 1-5. In general, the scratch padis for storing non-DS encoded intermediate results and the intermediateresult storage is for storing DS encoded intermediate results.

FIGS. 33-38 are schematic block diagrams of the distributed storage andtask network (DSTN) module performing the example of FIG. 30. In FIG.33, the DSTN module accesses the data 92 and partitions it into aplurality of partitions 1-z in accordance with distributed storage andtask network (DST) allocation information. For each data partition, theDSTN identifies a set of its DT (distributed task) execution modules 90to perform the task (e.g., identify non-words (i.e., not in a referencedictionary) within the data partition) in accordance with the DSTallocation information. From data partition to data partition, the setof DT execution modules 90 may be the same, different, or a combinationthereof (e.g., some data partitions use the same set while other datapartitions use different sets).

For the first data partition, the first set of DT execution modules(e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DST allocation information ofFIG. 32) executes task 1_1 to produce a first partial result 102 ofnon-words found in the first data partition. The second set of DTexecution modules (e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DSTallocation information of FIG. 32) executes task 1_1 to produce a secondpartial result 102 of non-words found in the second data partition. Thesets of DT execution modules (as per the DST allocation information)perform task 1_1 on the data partitions until the “z” set of DTexecution modules performs task 1_1 on the “zth” data partition toproduce a “zth” partial result 102 of non-words found in the “zth” datapartition.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results toproduce the first intermediate result (R1-1), which is a list ofnon-words found in the data. For instance, each set of DT executionmodules 90 stores its respective partial result in the scratchpad memoryof DST execution unit 1 (which is identified in the DST allocation ormay be determined by DST execution unit 1). A processing module of DSTexecution 1 is engaged to aggregate the first through “zth” partialresults to produce the first intermediate result (e.g., R1_1). Theprocessing module stores the first intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the first intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of non-words is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the firstintermediate result (R1-1) into a plurality of partitions (e.g., R1-1_1through R1-1_m). If the first intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the first intermediate result, or for the firstintermediate result, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 34, the DSTN module is performing task 1_2 (e.g., find uniquewords) on the data 92. To begin, the DSTN module accesses the data 92and partitions it into a plurality of partitions 1-z in accordance withthe DST allocation information or it may use the data partitions of task1_1 if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_2 inaccordance with the DST allocation information. From data partition todata partition, the set of DT execution modules may be the same,different, or a combination thereof. For the data partitions, theallocated set of DT execution modules executes task 1_2 to produce apartial results (e.g., 1^(st) through “zth”) of unique words found inthe data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results102 of task 1_2 to produce the second intermediate result (R1-2), whichis a list of unique words found in the data 92. The processing module ofDST execution 1 is engaged to aggregate the first through “zth” partialresults of unique words to produce the second intermediate result. Theprocessing module stores the second intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the second intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of unique words is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the secondintermediate result (R1-2) into a plurality of partitions (e.g., R1-2_1through R1-2_m). If the second intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the second intermediate result, or for the secondintermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 35, the DSTN module is performing task 1_3 (e.g., translate) onthe data 92. To begin, the DSTN module accesses the data 92 andpartitions it into a plurality of partitions 1-z in accordance with theDST allocation information or it may use the data partitions of task 1_1if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_3 inaccordance with the DST allocation information (e.g., DT executionmodules 1_1, 2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1through 2_4 and DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2translate data partitions 2_5 through 2_z). For the data partitions, theallocated set of DT execution modules 90 executes task 1_3 to producepartial results 102 (e.g., 1^(st) through “zth”) of translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 1_3 to produce the third intermediate result (R1-3), which istranslated data. The processing module of DST execution 2 is engaged toaggregate the first through “zth” partial results of translated data toproduce the third intermediate result. The processing module stores thethird intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the third intermediate result (e.g., translateddata). To begin the encoding, the DST client module partitions the thirdintermediate result (R1-3) into a plurality of partitions (e.g., R1-3_1through R1-3_y). For each partition of the third intermediate result,the DST client module uses the DS error encoding parameters of the data(e.g., DS parameters of data 2, which includes 3/5 decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 2-6 per the DST allocationinformation).

As is further shown in FIG. 35, the DSTN module is performing task 1_4(e.g., retranslate) on the translated data of the third intermediateresult. To begin, the DSTN module accesses the translated data (from thescratchpad memory or from the intermediate result memory and decodes it)and partitions it into a plurality of partitions in accordance with theDST allocation information. For each partition of the third intermediateresult, the DSTN identifies a set of its DT execution modules 90 toperform task 1_4 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated totranslate back partitions R1-3_1 through R1-3_4 and DT execution modules1_2, 2_2, 6_1, 7_1, and 7_2 are allocated to translate back partitionsR1-3_5 through R1-3_z). For the partitions, the allocated set of DTexecution modules executes task 1_4 to produce partial results 102(e.g., 1^(st) through “zth”) of re-translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_4 to produce the fourth intermediate result (R1-4), which isretranslated data. The processing module of DST execution 3 is engagedto aggregate the first through “zth” partial results of retranslateddata to produce the fourth intermediate result. The processing modulestores the fourth intermediate result as non-DS error encoded data inthe scratchpad memory or in another section of memory of DST executionunit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the fourth intermediate result (e.g., retranslateddata). To begin the encoding, the DST client module partitions thefourth intermediate result (R1-4) into a plurality of partitions (e.g.,R1-4_1 through R1-4_z). For each partition of the fourth intermediateresult, the DST client module uses the DS error encoding parameters ofthe data (e.g., DS parameters of data 2, which includes 3/5 decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 3-7 per the DST allocationinformation).

In FIG. 36, a distributed storage and task network (DSTN) module isperforming task 1_5 (e.g., compare) on data 92 and retranslated data ofFIG. 35. To begin, the DSTN module accesses the data 92 and partitionsit into a plurality of partitions in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. The DSTN module also accesses the retranslateddata from the scratchpad memory, or from the intermediate result memoryand decodes it, and partitions it into a plurality of partitions inaccordance with the DST allocation information. The number of partitionsof the retranslated data corresponds to the number of partitions of thedata.

For each pair of partitions (e.g., data partition 1 and retranslateddata partition 1), the DSTN identifies a set of its DT execution modules90 to perform task 1_5 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_5 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results oftask 1_5 to produce the fifth intermediate result (R1-5), which is thelist of incorrectly translated words and/or phrases. In particular, theprocessing module of DST execution 1 is engaged to aggregate the firstthrough “zth” partial results of the list of incorrectly translatedwords and/or phrases to produce the fifth intermediate result. Theprocessing module stores the fifth intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the fifth intermediate result. To begin theencoding, the DST client module partitions the fifth intermediate result(R1-5) into a plurality of partitions (e.g., R1-5_1 through R1-5_z). Foreach partition of the fifth intermediate result, the DST client moduleuses the DS error encoding parameters of the data (e.g., DS parametersof data 2, which includes 3/5 decode threshold/pillar width ratio) toproduce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 1-5 per the DST allocation information).

As is further shown in FIG. 36, the DSTN module is performing task 1_6(e.g., translation errors due to non-words) on the list of incorrectlytranslated words and/or phrases (e.g., the fifth intermediate resultR1-5) and the list of non-words (e.g., the first intermediate resultR1-1). To begin, the DSTN module accesses the lists and partitions theminto a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-1_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_6 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_6 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases due to non-words.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 1_6 to produce the sixth intermediate result (R1-6), which is thelist of incorrectly translated words and/or phrases due to non-words. Inparticular, the processing module of DST execution 2 is engaged toaggregate the first through “zth” partial results of the list ofincorrectly translated words and/or phrases due to non-words to producethe sixth intermediate result. The processing module stores the sixthintermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the sixth intermediate result. To begin theencoding, the DST client module partitions the sixth intermediate result(R1-6) into a plurality of partitions (e.g., R1-6_1 through R1-6_z). Foreach partition of the sixth intermediate result, the DST client moduleuses the DS error encoding parameters of the data (e.g., DS parametersof data 2, which includes 3/5 decode threshold/pillar width ratio) toproduce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 2-6 per the DST allocation information).

As is still further shown in FIG. 36, the DSTN module is performing task1_7 (e.g., correctly translated words and/or phrases) on the list ofincorrectly translated words and/or phrases (e.g., the fifthintermediate result R1-5) and the list of unique words (e.g., the secondintermediate result R1-2). To begin, the DSTN module accesses the listsand partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-2_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_7 in accordance with the DST allocation information(e.g., DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2). For each pairof partitions, the allocated set of DT execution modules executes task1_7 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of correctly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_7 to produce the seventh intermediate result (R1-7), which is thelist of correctly translated words and/or phrases. In particular, theprocessing module of DST execution 3 is engaged to aggregate the firstthrough “zth” partial results of the list of correctly translated wordsand/or phrases to produce the seventh intermediate result. Theprocessing module stores the seventh intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the seventh intermediate result. To begin theencoding, the DST client module partitions the seventh intermediateresult (R1-7) into a plurality of partitions (e.g., R1-7_1 throughR1-7_z). For each partition of the seventh intermediate result, the DSTclient module uses the DS error encoding parameters of the data (e.g.,DS parameters of data 2, which includes 3/5 decode threshold/pillarwidth ratio) to produce slice groupings. The slice groupings are storedin the intermediate result memory (e.g., allocated memory in thememories of DST execution units 3-7 per the DST allocation information).

In FIG. 37, the distributed storage and task network (DSTN) module isperforming task 2 (e.g., find specific words and/or phrases) on the data92. To begin, the DSTN module accesses the data and partitions it into aplurality of partitions 1-z in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. For each data partition, the DSTN identifies aset of its DT execution modules 90 to perform task 2 in accordance withthe DST allocation information. From data partition to data partition,the set of DT execution modules may be the same, different, or acombination thereof. For the data partitions, the allocated set of DTexecution modules executes task 2 to produce partial results 102 (e.g.,1^(st) through “zth”) of specific words and/or phrases found in the datapartitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 7 is assigned to process the first through “zth” partial results oftask 2 to produce task 2 intermediate result (R2), which is a list ofspecific words and/or phrases found in the data. The processing moduleof DST execution 7 is engaged to aggregate the first through “zth”partial results of specific words and/or phrases to produce the task 2intermediate result. The processing module stores the task 2intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 7.

DST execution unit 7 engages its DST client module to slice groupingbased DS error encode the task 2 intermediate result. To begin theencoding, the DST client module determines whether the list of specificwords and/or phrases is of a sufficient size to partition (e.g., greaterthan a Terabyte). If yes, it partitions the task 2 intermediate result(R2) into a plurality of partitions (e.g., R2_1 through R2_m). If thetask 2 intermediate result is not of sufficient size to partition, it isnot partitioned.

For each partition of the task 2 intermediate result, or for the task 2intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, and 7).

In FIG. 38, the distributed storage and task network (DSTN) module isperforming task 3 (e.g., find specific translated words and/or phrases)on the translated data (R1-3). To begin, the DSTN module accesses thetranslated data (from the scratchpad memory or from the intermediateresult memory and decodes it) and partitions it into a plurality ofpartitions in accordance with the DST allocation information. For eachpartition, the DSTN identifies a set of its DT execution modules toperform task 3 in accordance with the DST allocation information. Frompartition to partition, the set of DT execution modules may be the same,different, or a combination thereof. For the partitions, the allocatedset of DT execution modules 90 executes task 3 to produce partialresults 102 (e.g., 1^(st) through “zth”) of specific translated wordsand/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 5 is assigned to process the first through “zth” partial results oftask 3 to produce task 3 intermediate result (R3), which is a list ofspecific translated words and/or phrases found in the translated data.In particular, the processing module of DST execution 5 is engaged toaggregate the first through “zth” partial results of specific translatedwords and/or phrases to produce the task 3 intermediate result. Theprocessing module stores the task 3 intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 7.

DST execution unit 5 engages its DST client module to slice groupingbased DS error encode the task 3 intermediate result. To begin theencoding, the DST client module determines whether the list of specifictranslated words and/or phrases is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the task 3intermediate result (R3) into a plurality of partitions (e.g., R3_1through R3_m). If the task 3 intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the task 3 intermediate result, or for the task 3intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, 5, and 7).

FIG. 39 is a diagram of an example of combining result information intofinal results 104 for the example of FIG. 30. In this example, theresult information includes the list of specific words and/or phrasesfound in the data (task 2 intermediate result), the list of specifictranslated words and/or phrases found in the data (task 3 intermediateresult), the list of non-words found in the data (task 1 firstintermediate result R1-1), the list of unique words found in the data(task 1 second intermediate result R1-2), the list of translation errorsdue to non-words (task 1 sixth intermediate result R1-6), and the listof correctly translated words and/or phrases (task 1 seventhintermediate result R1-7). The task distribution module provides theresult information to the requesting DST client module as the results104.

FIG. 40A is a schematic block diagram of an embodiment of adecentralized agreement module 350 that includes a set of deterministicfunctions 1-N, a set of normalizing functions 1-N, a set of scoringfunctions 1-N, and a ranking function 352. Each of the deterministicfunction, the normalizing function, the scoring function, and theranking function 352, may be implemented utilizing the processing module84 of FIG. 3. The decentralized agreement module 350 may be implementedutilizing any module and/or unit of a dispersed storage network (DSN).For example, the decentralized agreement module is implemented utilizingthe distributed storage and task (DST) client module 34 of FIG. 1.

The decentralized agreement module 350 functions to receive a rankedscoring information request 354 and to generate ranked scoringinformation 358 based on the ranked scoring information request 354 andother information. The ranked scoring information request 354 includesone or more of an asset identifier (ID) 356 of an asset associated withthe request, an asset type indicator, one or more location identifiersof locations associated with the DSN, one or more corresponding locationweights, and a requesting entity ID. The asset includes any portion ofdata associated with the DSN including one or more asset types includinga data object, a data record, an encoded data slice, a data segment, aset of encoded data slices, and a plurality of sets of encoded dataslices. As such, the asset ID 356 of the asset includes one or more of adata name, a data record identifier, a source name, a slice name, and aplurality of sets of slice names.

Each location of the DSN includes an aspect of a DSN resource. Examplesof locations includes one or more of a storage unit, a memory device ofthe storage unit, a site, a storage pool of storage units, a pillarindex associated with each encoded data slice of a set of encoded dataslices generated by an information dispersal algorithm (IDA), a DSTclient module 34 of FIG. 1, a DST processing unit 16 of FIG. 1, a DSTintegrity processing unit 20 of FIG. 1, a DSTN managing unit 18 of FIG.1, a user device 12 of FIG. 1, and a user device 14 of FIG. 1.

Each location is associated with a location weight based on one or moreof a resource prioritization of utilization scheme and physicalconfiguration of the DSN. The location weight includes an arbitrary biaswhich adjusts a proportion of selections to an associated location suchthat a probability that an asset will be mapped to that location isequal to the location weight divided by a sum of all location weightsfor all locations of comparison. For example, each storage pool of aplurality of storage pools is associated with a location weight based onstorage capacity. For instance, storage pools with more storage capacityare associated with higher location weights than others. The otherinformation may include a set of location identifiers and a set oflocation weights associated with the set of location identifiers. Forexample, the other information includes location identifiers andlocation weights associated with a set of memory devices of a storageunit when the requesting entity utilizes the decentralized agreementmodule 350 to produce ranked scoring information 358 with regards toselection of a memory device of the set of memory devices for accessinga particular encoded data slice (e.g., where the asset ID includes aslice name of the particular encoded data slice).

The decentralized agreement module 350 outputs substantially identicalranked scoring information for each ranked scoring information requestthat includes substantially identical content of the ranked scoringinformation request. For example, a first requesting entity issues afirst ranked scoring information request to the decentralized agreementmodule 350 and receives first ranked scoring information. A secondrequesting entity issues a second ranked scoring information request tothe decentralized agreement module and receives second ranked scoringinformation. The second ranked scoring information is substantially thesame as the first ranked scoring information when the second rankedscoring information request is substantially the same as the firstranked scoring information request.

As such, two or more requesting entities may utilize the decentralizedagreement module 350 to determine substantially identical ranked scoringinformation. As a specific example, the first requesting entity selectsa first storage pool of a plurality of storage pools for storing a setof encoded data slices utilizing the decentralized agreement module 350and the second requesting entity identifies the first storage pool ofthe plurality of storage pools for retrieving the set of encoded dataslices utilizing the decentralized agreement module 350.

In an example of operation, the decentralized agreement module 350receives the ranked scoring information request 354. Each deterministicfunction performs a deterministic function on a combination and/orconcatenation (e.g., add, append, interleave) of the asset ID 356 of theranked scoring information request 354 and an associated location ID ofthe set of location IDs to produce an interim result. The deterministicfunction includes at least one of a hashing function, a hash-basedmessage authentication code function, a mask generating function, acyclic redundancy code function, hashing module of a number oflocations, consistent hashing, rendezvous hashing, and a spongefunction. As a specific example, deterministic function 2 appends alocation ID 2 of a storage pool 2 to a source name as the asset ID toproduce a combined value and performs the mask generating function onthe combined value to produce interim result 2.

With a set of interim results 1-N, each normalizing function performs anormalizing function on a corresponding interim result to produce acorresponding normalized interim result. The performing of thenormalizing function includes dividing the interim result by a number ofpossible permutations of the output of the deterministic function toproduce the normalized interim result. For example, normalizing function2 performs the normalizing function on the interim result 2 to produce anormalized interim result 2.

With a set of normalized interim results 1-N, each scoring functionperforms a scoring function on a corresponding normalized interim resultto produce a corresponding score. The performing of the scoring functionincludes dividing an associated location weight by a negative log of thenormalized interim result. For example, scoring function 2 divideslocation weight 2 of the storage pool 2 (e.g., associated with locationID 2) by a negative log of the normalized interim result 2 to produce ascore 2.

With a set of scores 1-N, the ranking function 352 performs a rankingfunction on the set of scores 1-N to generate the ranked scoringinformation 358. The ranking function includes rank ordering each scorewith other scores of the set of scores 1-N, where a highest score isranked first. As such, a location associated with the highest score maybe considered a highest priority location for resource utilization(e.g., accessing, storing, retrieving, etc., the given asset of therequest). Having generated the ranked scoring information 358, thedecentralized agreement module 350 outputs the ranked scoringinformation 358 to the requesting entity.

FIG. 40B is a flowchart illustrating an example of selecting a resource.The method begins or continues at step 360 where a processing module(e.g., of a decentralized agreement module) receives a ranked scoringinformation request from a requesting entity with regards to a set ofcandidate resources. For each candidate resource, the method continuesat step 362 where the processing module performs a deterministicfunction on a location identifier (ID) of the candidate resource and anasset ID of the ranked scoring information request to produce an interimresult. As a specific example, the processing module combines the assetID and the location ID of the candidate resource to produce a combinedvalue and performs a hashing function on the combined value to producethe interim result.

For each interim result, the method continues at step 364 where theprocessing module performs a normalizing function on the interim resultto produce a normalized interim result. As a specific example, theprocessing module obtains a permutation value associated with thedeterministic function (e.g., maximum number of permutations of outputof the deterministic function) and divides the interim result by thepermutation value to produce the normalized interim result (e.g., with avalue between 0 and 1).

For each normalized interim result, the method continues at step 366where the processing module performs a scoring function on thenormalized interim result utilizing a location weight associated withthe candidate resource associated with the interim result to produce ascore of a set of scores. As a specific example, the processing moduledivides the location weight by a negative log of the normalized interimresult to produce the score.

The method continues at step 368 where the processing module rank ordersthe set of scores to produce ranked scoring information (e.g., ranking ahighest value first). The method continues at step 370 where theprocessing module outputs the ranked scoring information to therequesting entity. The requesting entity may utilize the ranked scoringinformation to select one location of a plurality of locations.

FIG. 41A is a schematic block diagram of another embodiment of adecentralized agreement module 372 that includes a first and seconddeterministic function of FIG. 40A, a first and second normalizingfunction of FIG. 40A, a first and second scoring function of FIG. 40A,and a ranking function 374. The ranking function 374 may be implementedutilizing the ranking function 352 of FIG. 40A.

In an example of operation, each deterministic function functions tocombined and/or concatenate a branch identifier (ID), an asset ID 378 ofa ranked scoring information request 376, and a current node ID toproduce a combined value and to perform the deterministic function ofFIG. 40A on the combined value to produce an interim result. Eachnormalizing function performs the normalizing function of FIG. 40A oneach interim result to produce normalized interim results.

Each scoring function performs a scoring function on an associatednormalized interim result using an associated branch weight to produce acorresponding score. The scoring function includes dividing the branchweight by a negative log of the normalized interim result to produce thescore.

The ranking function 374 performs the ranking function of FIG. 40A onthe scores to produce ranked scoring information 380. The ranked scoringinformation 380 indicates a highest score ranked first where a firstranked location may be considered a highest priority branch to continuean analysis until reaching a lowest level of nodes of a location treestructure that are associated with a location of a high score foraccessing a DSN resource utilizing the given asset of the ranked scoringinformation request. The location tree structure is discussed in greaterdetail with reference to FIG. 41B.

FIG. 41B is a diagram of an embodiment of a location tree structure 382utilized to represent locations and associated location weights of adispersed storage network (DSN). The location tree structure 382includes a root node connected by one or more branches to next levelnodes which may be connected by one or more further branches to one ormore further nodes etc., until a lowest level of nodes. Nodes of thelowest level are associated with unique locations of a dispersed storagenetwork (DSN) (e.g., a set of storage units, a set of memory devices, aset of storage pools, a set of sites, a set of processing units, etc). Asum of location weights of each of the lowest level nodes produces atotal location weight of the location tree structure, represented withinthe root node. In an example, the location tree structure 382 includes aroot node a with branch b to node b and branch c to node c. Node brepresents location weight 3 of lowest level nodes d and e via branchesd and e respectively. Node d is associated with location A and node e isassociated with location B. Node c represents location weight 7 oflowest level nodes f and g via branches f and g respectively. Node f isassociated with location C and node g is associated with location D.

The location tree structure 382 may be utilized in combination with thedecentralized agreement module 372 of FIG. 41A in a recursive manner totraverse down the location tree structure 382 to identify a locationassociated with a highest score. In an example of operation, thedecentralized agreement module receives a ranked scoring informationrequest from a requesting entity, where the request includes an assetID. Having received the asset ID, the decentralized agreement moduleobtains the location tree structure 382 associated with the rankedscoring information request. The obtaining includes at least one ofperforming a lookup, receiving, and identifying based on the asset ID.For example, when receiving the ranked scoring information request thatincludes an asset ID associated with storage pool selection, thedecentralized agreement module identifies a corresponding location treestructure associated with storage pool selection as the location treestructure 382.

Having obtained the location tree structure 382, the decentralizedagreement module performs steps of a method, where an initial stepincludes entering a loop, where for two or more branches at each levelof the location tree descending from the root node from a current nodeassociated with each loop, performs the deterministic function on anassociated branch ID, the asset ID, and a current node ID of the currentnode to produce a corresponding interim result. For example, thedecentralized agreement module performs deterministic functions forbranches b and c to produce interim results 1 and 2 when the currentnode is the root node a.

For each interim result, the decentralized agreement module performs thenormalizing function on the interim results to produce normalizedinterim results. For each normalized interim result, the decentralizedagreement module performs the scoring function on the normalized interimresult utilizing a branch weight associated with the normalized interimresult to produce a score of a set of scores. The branch weight includesa sum of all location weights of all nodes extending from an associatedbranch. For example, the decentralized agreement module divides a branchweight of 3 of branch b by a negative log of the normalized interimresult for branch b to produce score 1 and divides a branch weight of 7by a negative log of the normalized interim result for branch c toproduce score 2.

Having produced the scores, for each normalized interim result, thedecentralized agreement module performs the ranking function to rankorder the set of scores by associated location a deed to produce rankedscoring information to identify a next branch or location associatedwith a high score. The decentralized agreement module continues the loopwhen identifying a next branch. The decentralized agreement module exitsthe loop when identifying the location and outputs the ranked scoringinformation to the requesting entity (e.g., identifying a locationassociated with a highest score).

FIG. 41C is a flowchart illustrating another example of selecting aresource, which include similar steps to FIG. 40B. The method begins orcontinues at step 384 where a processing module (e.g., of a distributedstorage and task (DST) client module) receives a ranked scoringinformation request from a requesting entity with regards to a set ofcandidate resources. The ranked scoring information request includes anasset ID and may further include identity of a location tree structureassociated with the asset ID.

The method continues at step 386 where the processing module obtains alocation tree structure associated with the ranked scoring informationrequest. The obtaining includes at least one of retrieving, issuing aquery, and receiving a query response. For example, the processingmodule accesses a dispersed hierarchical index stored within a dispersedstorage network (DSN) based on an asset type of the asset ID to identifyand recover the location tree structure from DSN memory.

For each of two or more branches of the location tree structure from acurrent node of the location tree structure, the method continues atstep 388 where the processing module performs a deterministic functionon a branch ID of the branch, the asset ID of the request, and a currentnode ID of the current node to produce a corresponding interim result.For example, the processing module combines the branch ID, the asset ID,and the current node ID to produce a combined value and performs asponge function on the combined value to produce the interim result. Foreach interim result, the method continues with step 364 of FIG. 40Bwhere the processing module performs a normalizing function on theinterim result to produce a normalized interim result.

For each normalized interim result, the method continues at step 390where the processing module performs a scoring function on thenormalized interim result utilizing a branch weight associated with theinterim result to produce a score of a set of scores for a correspondingportion of the location tree structure. For example, the processingmodule divides a corresponding branch weight by a negative log of thenormalized interim result to produce the score. The method continueswith step 368 of FIG. 40B where the processing module rank orders theset of scores to produce ranked scoring information.

The method continues at step 392 where the processing module identifiesone of a next branch and a location based on the ranked scoringinformation. For example, the processing module identifies a highestscore and an associated branch or location. As another example, theprocessing module identifies scores that compare favorably to a scorethreshold level (e.g., scores greater than the score threshold level).The method loops back to step 388 when identifying the next branch. Themethod continues to step 370 of FIG. 40B when identifying the location.The method continues with step 370 of FIG. 40B where the processingmodule outputs the ranked scoring information to the requesting entitywhen identifying the location.

FIG. 42A is a diagram of another embodiment of a location tree structure394 illustrating an example of modifying a resource pool. In particular,FIG. 42A illustrates an example of adding a new location D utilizing amodifying approach that includes reserving a node. Prior to themodifying, the location tree structure 394 includes lowest level nodesd-f that are associated with locations A, B, and C of a dispersedstorage network (DSN) and a reserved node g that is not associated witha location and is associated with a zero location weight.

When modifying the resource pool to include the new location D, thereserved node of the location tree structure is associated with thelocation D and a location weight of 4 is associated with the node g.Having associated the location weight of 4, parent nodes are updated toinclude the additional location weight. For example, a prior locationweight of node c is updated from 3 to 7 to include the additionallocation weight of 4 and root node 1 is updated from 6 to 10 to includethe additional location weight of updated node c.

When updating the total location weight of an updated location treestructure 396, a proportionate number of associated assets of thelocations may be migrated from locations A-C to location D. A maximumstability property is maintained by not moving assets between locationsA-C. For example, 4 of every 10 assets are migrated from each of thelocations A-C to the location D. For instance, a decentralized agreementfunction may be utilized to identify the assets for migration utilizingidentifiers of the assets, previous location weights, and the updatedlocation weights.

FIG. 42B is a diagram of another embodiment of a location tree structure398 illustrating another example of modifying a resource pool. Inparticular, FIG. 42B illustrates an example of adding a new location Eutilizing another modifying approach that includes adding branches andfurther reserved nodes. When updating, a new root node is generated andone or more new node levels and associated branches are generated totraverse from the new root node to a new node associated with the newlocation E. The new root node becomes a parent node to the previous rootnode. Additional reserved nodes may be added to support subsequentexpansion.

When updating, a location weight of the new location is added to aprevious total location weight of the location tree structure prior toupdating to produce a new total location weight of an updated locationtree structure. For example, a new total location weight of 12 resultswhen adding the new location E with a location weight of 2 to theprevious total location weight of 10.

When updating the total location weight of the location tree structure398 to produce an updated location tree structure 400, a proportionatenumber of associated assets of the locations may be migrated fromlocations A-D to location E. A maximum stability property is maintainedby not moving assets between locations A-D. For example, 2 of every 12assets are migrated from each of the locations A-D to the location E.For instance, a decentralized agreement function may be utilized toidentify the assets for migration utilizing identifiers of the assets,previous location weights, and the updated location weights.

FIG. 42C is a diagram of another embodiment of a location tree structure402 illustrating another example of modifying a resource pool. Inparticular, FIG. 42C illustrates an example of replacing location A witha new location D utilizing yet another modifying approach that includesutilizing a reserved node and mapping a previous node to the utilizedreserve note. When updating, the reserve node is selected and associatedwith the new location D with a location weight of 8.

Location weight associated with the reserved node is updated to includethe location weight of the new location minus the location weight of thenode being replaced. For example, the reserved node associated withlocation D is updated from 0 to 7 when the location weight of location Dis 8 and the location weight of location A was 1. The node previouslyassociated with location A is now associated with the new location D.The total location weight of a resulting updated location tree structure404 is updated to include the location weight of the location minus thelocation weight of the location being replaced. For instance, the totallocation weight is updated from 6 to 13.

All assets associated with location A are migrated to location D. aproportionate number of other assets associated with locations B and Care migrated to location D. For example, 7 of 13 assets associated withlocations B and C are migrated to location D.

FIG. 42D is a flowchart illustrating an example of modifying a resourcepool. The method begins or continues at step 406 where a processingmodule (e.g., of a distributed storage and task (DST) client module)determines to add a new resource to a resource pool, where the resourcepool includes a plurality of resources each associated with a locationweight. The determining may be based on one or more of a resourceutilization level, a resource schedule, a request, and apredetermination.

The method continues at step 408 where the processing module obtains alocation tree structure associated with a resource pool. For example,the processing module accesses a dispersed storage network (DSN) memoryto recover the location tree structure based on identity of the resourcepool. The method continues at step 410 where the processing moduledetermines a location weight for the new resource. The determining maybe based on one or more of capabilities of the resource, capabilities ofresources of the resource pool, location weights of each of theresources of the resource pool, a request, and a predetermination.

The method continues at step 412 where the processing module determinesa resource pool expansion approach based on the location tree structure.For example, the processing module indicates to utilize an existing nodeof the location tree structure when a reserved node is available. Asanother example, the processing module indicates to add a new node whenthe reserve node is not available. The method branches to step 420 whenthe approach includes adding a new node. The method continues to step414 when the approach includes using the reserved node.

The method continues at step 414 where the processing module associatesthe new resource with a reserve node of the location tree structure whenusing the reserve node. For example, the processing module selects thereserve node, updates the node to point to the new resource, and updatesthe node from a zero location weight to the location weight for the newresource.

For each parent node of the reserved node, the method continues at step416 where the processing module updates a weight of the parent node toinclude the location weight of the new resource. Alternatively, or inaddition to, the processing module may associate the new resource with anode associated with a resource being replaced. The updating includesadding the weight of the parent node to the location weight of the newresource to produce an updated weight of the parent node.

The method continues at step 418 where the processing modulere-associates assets (e.g., resource objects) corresponding to at leastsome of the resources of the resource pool to the new resource. Forexample, the processing module migrates a proportionate number of theassets from the resources to the new resource. When replacing aresource, the processing module migrates assets of the resource beingreplaced to the new resource.

When the resource pool expansion approach includes the adding the newnode, the method continues at step 420 where the processing modulegenerates a super-location tree structure that includes the locationtree structure. For example, the processing module generates one or morereserve nodes, generates one or more parent nodes, generates a new rootnode to include connectivity to a root node of the location treestructure and to at least one newly generated parent node. The methodcontinues at step 422 where the processing module associates theresource with a reserved node of the super-location tree structure. Forexample, the processing module selects the reserve node, updates theselected reserve node to point to the new resource, and updates the nodefrom a zero weight to the location weight for the new resource. Themethod branches to step 416.

FIGS. 43A through 43D are a schematic block diagram of an embodiment ofa dispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1,and the distributed storage and task network (DSTN) module 22 of FIG. 1.The DST processing unit 16 includes a decentralized agreement module 430and the DST client module 34 of FIG. 1. The decentralized agreementmodule 430 may be implemented utilizing the decentralized agreementmodule 350 of FIG. 40A. The DSTN module 22 includes a plurality of DSTexecution (EX) unit pools 1-P. Each DST execution unit pool includes aplurality of N DST execution units. Each DST execution unit includes aplurality of memories 1-M. Each DST execution unit may be implementedutilizing the DST execution unit 36 of FIG. 1. Each DST execution unitmay be implemented at one site of S sites per storage pool. Each DSTexecution unit may be associated with at least one pillar of N pillarsassociated with an information dispersal algorithm (IDA). Each site maynot include every pillar and a given pillar may be implemented at morethan one site. Hereafter, a DST execution unit may be interchangeablyreferred to as a storage unit, a set of DST execution units may beinterchangeably referred to as a set of storage units, and the DSTNmodule 22 may be interchangeably referred to as a DSN memory.

The DSN functions to access the DSN memory. The accessing may includeone or more of receiving data access requests 432, selecting resourcesof at least one DST execution unit pool for data access, utilizing theselected DST execution unit pool for the data access, and issuing a dataaccess response based on the data access. The selecting of the resourcesincludes utilizing a decentralized agreement function of thedecentralized agreement module 430, where a plurality of locations areranked against each other for each of one or more resource levels. Theselecting may include selecting one storage pool of the plurality ofstorage pools, selecting DST execution units at various sites of theplurality of sites, selecting a memory of the plurality of memories foreach DST execution unit, and selecting combinations of memories, DSTexecution units, sites, pillars, and storage pools.

FIG. 43A illustrates steps of an example of operation of the accessingwhere the DST processing unit 16 receives a DSN access request regardingat least one data segment of a data object. For example, the DST clientmodule 34 receives the data access request 432 from a requesting entity,where the data access request 432 includes at least one of a store datarequest, a retrieve data request, a delete data request, a data name,and a requesting entity identifier (ID).

Having received the data access request 432, the DST client module 34determines a DSN address associated with the data access request 432.The DSN address includes at least one of a source name (e.g., includinga vault identifier and an object number associated with a data name ofthe data object), a data segment identifier (ID) of the at least onedata segment, a set of slice names associated with the at least one datasegment, and a plurality of sets of slice names associated with aplurality of sets of encoded data slices associated with storage of thedata object. The determining includes at least one of generating (e.g.,for the store data request) and retrieving (e.g., from a DSN directory)based on the data name (e.g., for the retrieve data request). Forexample, the DST client module 34 utilizes the data name to perform alookup in the DSN directory to identify an associated source name as theDSN address.

Having determined the DSN address, the DST client module 34 performs ascoring function using one or more properties of the DSN access request(e.g., an associated address) and one or more properties of DSN memoryof the DSN (e.g., identifiers of entities of a resource level, weightingfactor of the entities) to produce a storage scoring resultant, wherethe DSN memory includes the plurality of storage units that arelogically arranged into the plurality of storage pools.

As a specific example of the performing the scoring function, the DSTclient module 34 selects the resource level (e.g., storage pool level,storage unit level, memory device level, default may include the storagepool level, etc.), selects the one or more properties of the DSN memoryfrom a plurality of properties of the DSN memory based on the selectedresource level, calculates, based on the selected resource level, aplurality of storage values based on the one or more properties of theDSN access request and the one or more properties of DSN memory, andperforms a ranking function of the plurality of storage values toproduce the storage scoring resultant. For instance, the DST clientmodule 34 selects the storage pool level, selects identifiers andweighting factors of the storage pools 1-P (e.g., interprets systemregistry information), issues a ranked scoring information request 434to the decentralized agreement module 430, where the ranked scoringinformation request 434 includes the DSN address, and the identifiersand weighting factors of the storage pools 1-P, and receives, from thedecentralized agreement module 430, ranked scoring information 436 thatincludes the plurality of storage values in a ranked ordered fashion toproduce the storage scoring resultant.

Having produced the storage scoring resultant, the DST client module 34utilizes the storage scoring resultant to identify a set of storageunits of the plurality of storage units affiliated with a given storagepool of the plurality of storage pools. For example, the DST clientmodule 34 interprets the ranked scoring information 436 to identify ahighest storage value of the plurality of storage values, where the highstorage value is associated with at least one set of storage units ofthe storage pool 1. The method to produce the storage scoring resultantis discussed in greater detail with reference to FIGS. 43B-C.

FIG. 43B is a schematic block diagram of an embodiment of thedistributed storage and task (DST) processing unit 16 of FIG. 43A thatincludes the DST client module 34 of FIG. 43A and the decentralizedagreement module 430 of FIG. 43A. The decentralized agreement module 430of FIG. 43B includes a plurality of deterministic functions 1-P, aplurality of normalizing functions 1-P, a plurality scoring functions1-P, and the ranking function 352 of FIG. 40A. Each deterministicfunction may be implemented utilizing the deterministic function of thedecentralized agreement module 350 of FIG. 40A. Each normalizingfunction may be implemented utilizing the normalizing function of thedecentralized agreement module 350 of FIG. 40A. Each scoring functionmay be implemented utilizing the scoring function of the decentralizedagreement module 350 of FIG. 40A.

FIG. 43B illustrates further steps of the example of operation of theaccessing where the DST client module 34 receives a data access requestA and selects a storage pool level indication as the resource level.Having selected the storage pool level, the DST client module 34 selectsa storage pool identifier and a storage pool weighting factor for eachof the plurality of storage pools 1-P to produce a plurality of storagepool identifiers and a plurality of storage pool weighting factors,where the one or more properties of DSN memory includes the plurality ofstorage pool identifiers and the plurality of storage pool weightingfactors. For instance, the DST client module 34 accesses the systemregistry information to extract the storage pool identifiers and storagepool weighting factors.

Having produced the storage pool identifiers and storage pool weightingfactors, the DST client module 34 selects a source name 438 of the DSNaccess request A as the one or more properties of the DSN accessrequest. Having selected the source name 438, the DST client module 34issues a ranked scoring information request A for the storage pool levelto the decentralized agreement module 430, where the request includesthe source name 438, the identifiers of the storage pools 1-P, and theweighting factors of the storage pools 1-P.

Having received the ranked scoring information request A, thedecentralized agreement module 430 performs a series of functions on thesource name 438 based on the plurality of storage pool identifiers andthe plurality of storage pool weighting factors to produce a pluralityof storage values. A series of the series of functions includes one ormore of a deterministic function of the source name 438 and one of thestorage pool identifiers to produce an interim result, a normalizingfunction of the interim result to produce a normalized interim result,and a scoring function of the normalized interim result and acorresponding one of the storage pool weighting factors to produce astorage value of the plurality of storage values. For example,deterministic function 1 performs a mask generating function on aconcatenation of the source name 438 and the storage pool 1 ID toproduce interim result 1, normalizing function 1 performs a normalizingfunction by dividing the interim result 1 by a number of possiblepermutations of the output of the deterministic function 1 to produce anormalized interim result 1, and the scoring function 1 performs ascoring function by dividing pool 1 weight by a negative log of thenormalized interim result 1 to produce a score 1, etc.

Having produced the plurality of storage values, the decentralizedagreement module 430 performs a ranking function of the plurality ofstorage value to produce storage pool ranked scoring information A thatincludes the storage scoring resultant. For example, the rankingfunction 352 rank orders scores 1-N along with storage pool identifiersby score value to produce the storage pool ranked scoring information A.For instance, the storage pool ranked scoring information A indicatesthat storage pool 1 is associated with a highest ranking (e.g., 1) andas such, the storage pool 1 is to be considered a highest prioritylocation for accessing the data object.

FIG. 43C is a schematic block diagram of an embodiment of thedistributed storage and task (DST) processing unit 16 of FIG. 43A thatincludes the DST client module 34 of FIG. 43A and the decentralizedagreement module 430 of FIG. 43A. The decentralized agreement module 430of FIG. 43C includes a plurality of deterministic functions 1-SN, aplurality of normalizing functions 1-SN, a plurality scoring functions1-SN, and the ranking function 352 of FIG. 40A. Each deterministicfunction may be implemented utilizing the deterministic function of thedecentralized agreement module 350 of FIG. 40A. Each normalizingfunction may be implemented utilizing the normalizing function of thedecentralized agreement module 350 of FIG. 40A. Each scoring functionmay be implemented utilizing the scoring function of the decentralizedagreement module 350 of FIG. 40A.

FIG. 43C illustrates further steps of the example of operation of theaccessing where the DST client module 34 selects a storage unit levelindication (e.g., a site-unit level) as a resource level. Havingselected the storage unit level indication, the DST client module 34selects a storage site-storage unit identifier and a storagesite-storage weighting factor for each of the plurality of storage unitsto produce a plurality of storage site-storage unit identifiers (e.g.,SN in number for S sites and N units per site) and a plurality ofstorage site-storage unit weighting factors, where the one or moreproperties of DSN memory includes the plurality of storage site-storageunit identifiers and the plurality of storage site-storage unitweighting factors. Having selected the identifiers and weightingfactors, the DST client module 34 selects the source name 438 of the DSNaccess request as the one or more properties of the DSN access request.Having selected the source name 438, the DST client module 34 issues aranked scoring information request A for site-units to decentralizedagreement module 430.

The decentralized agreement module 430 performs a series of functions onthe source name 438 based on the plurality of storage site-storage unitidentifiers and the plurality of storage site-storage unit weightingfactors to produce a plurality of storage values (e.g., scores 1-SN).For example, for each combination of site and storage unit (e.g., SNnumber), a deterministic function performs a hashing type deterministicfunction on an identifier of the combination of a site in the storageunit concatenated with the source name 438 to produce an interim result,the normalizing function performs the normalizing function on theinterim result to produce a normalized interim result, the scoringfunction performs a scoring function on the normalized interim resultand the weight associated with the combination of the site and thestorage unit to produce a score of the plurality of storage values(e.g., scores 1-SN).

Having produced the plurality of storage values, the decentralizedagreement module 430 performs a ranking function of the plurality ofstorage values to produce the storage scoring resultant. For example,the ranking function 352 ranks scores associated with sites and units byscore to produce site-unit ranked scoring information A. For instance,the ranking function 352 produces the storage scoring resultant thatindicates that a decode threshold number of highest ranked site unitcombinations includes a unit 1 at site 3, a unit 2 at site 3, a unit 3at site 4, a unit 4 at site number 4, a unit 5 at site 4, a unit 6 atsite 3, a unit 7 at site 4, a unit 8 at site 4, a unit 9 at site 3, anda unit 10 at site 4. Having produced the storage scoring resultant, thedecentralized agreement module 430 sends the storage scoring resultantto the DST client module 34.

Having received the storage scoring resultant, the DST client module 34utilizes the storage scoring resultant to identify a set of storageunits of the plurality of storage units affiliated with a given storagepool of the plurality of storage pools and a set of access requests 440to the set of storage units regarding the DSN access request. Forexample, the DST client module 34 identifies the highest ranked decodethreshold number of site-units, generates the set of access requests440, and sends the set of access request 440 to the identified set ofstorage units.

FIG. 43D illustrates further steps of the example of operation of theaccessing where, the DST processing unit 16, when the storage scoringresultant identifies a pillar width number of storage units, utilizesone or more properties of storage units in the pillar width number ofstorage units to identify at least a decode threshold number of storageunits. For example, the DST client module 34 rank orders the scores ofthe storage scoring resultant and identifies the decode threshold numberof storage units associated with highest scores. Having identified theat least a decode threshold number of storage units, the DST clientmodule 34 uses the decode threshold number of storage units as the setof storage units. For example, the DST client module 34 sends the set ofaccess requests to the set of storage units regarding the DSN accessrequest, where the set of storage units includes the decode thresholdnumber of storage units.

In an instance of the access, the DST client module 34 issues, via thenetwork 24, slice access requests 1-10 to storage units at sites 3 and 4of the storage pool 1, receives, via the network 24, slice accessresponses 1-10, and processes the received slice access responses 1-10to produce a data access response 442 (e.g., the data object whenretrieving the data object, storage confirmation when storing the dataobject). In a specific instance of sending the slice access requests1-10, the DST client module 34 sends the slice access request 1 to theunit 1 at the site 3, the request 2 to the unit 2 at the site 3, theslice access request 3 to the unit 3 at the site 4, the slice accessrequest 4 to the unit 4 at the site 4, the slice access request 5 to theunit 5 at the site 5, the slice access request 6 to the unit 6 at thesite 3, the slice access request 7 to the unit 7 at the site 4, theslice access request 8 to the unit 8 at the site 4, the slice accessrequest 9 to the unit 9 at the site 3, and the slice access request 10to the unit 10 at the site 4.

In addition to identifying the set of storage units (e.g., the storagepool 1), the DSN may continue to utilize the distributed agreementprotocol function to identify other DSN memory resource selections. Asspecific example of the identifying the other DSN memory resourceselections, when the storage scoring resultant identifies the givenstorage pool, the DST client module 34 utilizes one or more otherproperties of the DSN memory to identify storage units affiliated withthe given storage pool and uses the identified storage units as the setof storage units.

As another specific example of the identifying the other DSN memoryresource selections, when the storage scoring resultant identifiesparticular memory devices within storage units of the plurality ofstorage units, the DST client module 34 utilizes one or more propertiesof storage units in a pillar width number of storage units to identifyat least a decode threshold number of storage units, utilizes one ormore properties of memory devices within storage units of the at least adecode threshold number of storage units to identify the set of storageunits, and sends the set of access requests to the set of storage unitsregarding the DSN access request, where the set of access requestsincludes indications of particular memory devices within the set ofstorage units. The indications of particular memory devices are based onthe one or more properties of the memory devices.

FIG. 43E is a flowchart illustrating an example of accessing a dispersedstorage network (DSN) memory. In particular, a method is presented foruse in conjunction with one or more functions and features described inconjunction with FIGS. 1-39, 43A-D, and also FIG. 43E. The method beginsat step 450 where a processing module of a computing device of one ormore computing devices of a dispersed storage network (DSN) receives aDSN access request regarding at least one data segment of a data object.

The method continues at step 452 to where the processing module performsa scoring function (e.g., a distributed agreement protocol function)using one or more properties of the DSN access request and one or moreproperties of DSN memory of the DSN to produce a storage scoringresultant, where the DSN memory includes a plurality of storage unitsthat are logically arranged into a plurality of storage pools.

As a specific example of the performing the scoring function, theprocessing module selects a resource level, selects the one or moreproperties of the DSN memory from a plurality of properties of the DSNmemory based on the selected resource level, calculates, based on theselected resource level, a plurality of storage values based on the oneor more properties of the DSN access request and the one or moreproperties of DSN memory, and performs a ranking function of theplurality of storage values to produce the storage scoring resultant.

As another specific example of the performing the scoring function, theprocessing module selects a storage pool level indication as theresource level, selects a storage pool identifier and a storage poolweighting factor for each of the plurality of storage pools to produce aplurality of storage pool identifiers and a plurality of storage poolweighting factors, where the one or more properties of DSN memoryincludes the plurality of storage pool identifiers and the plurality ofstorage pool weighting factors, selects a source name of the DSN accessrequest as the one or more properties of the DSN access request, andselects a series of functions on the source name based on the pluralityof storage pool identifiers and the plurality of storage pool weightingfactors to produce a plurality of storage values.

As yet another specific example of the performing the scoring function,the processing module selects a storage unit level indication as aresource level, selects a storage site-storage unit identifier and astorage site-storage weighting factor for each of the plurality ofstorage units to produce a plurality of storage site-storage unitidentifiers and a plurality of storage site-storage unit weightingfactors, where the one or more properties of DSN memory includes theplurality of storage site-storage unit identifiers and the plurality ofstorage site-storage unit weighting factors, selects a source name ofthe DSN access request as the one or more properties of the DSN accessrequest, performs a series of functions on the source name based on theplurality of storage site-storage unit identifiers and the plurality ofstorage site-storage unit weighting factors to produce a plurality ofstorage values, and performs a ranking function of the plurality ofstorage value to produce the storage scoring resultant.

The method continues at step 454 where the processing module utilizesthe storage scoring resultant to identify a set of storage units of theplurality of storage units affiliated with a given storage pool of theplurality of storage pools (e.g., identifying storage units associatedwith highest values of the plurality of storage values). The methodcontinues at step 456 where the processing module sends a set of accessrequests to the set of storage units regarding the DSN access request.

In addition to identifying the set of storage units, the DSN maycontinue to utilize the distributed agreement protocol function toidentify other DSN memory resource selections. As a specific example,when the storage scoring resultant identifies the given storage pool,the processing module utilizes one or more other properties of the DSNmemory to identify storage units affiliated with the given storage pooland uses the identified storage units as the set of storage units. Asanother specific example of the identifying the other DSN memoryresource selections, when the storage scoring resultant identifies apillar width number of storage units, the processing module utilizes oneor more properties of storage units in the pillar width number ofstorage units to identify at least a decode threshold number of storageunits and uses the decode threshold number of storage units as the setof storage units.

As yet another example of the identifying the other DSN memory resourceselections, when the storage scoring resultant identifies particularmemory devices within storage units of the plurality of storage units,the processing module utilizes one or more properties of storage unitsin a pillar width number of storage units to identify at least a decodethreshold number of storage units, utilizes one or more properties ofmemory devices within storage units of the at least a decode thresholdnumber of storage units to identify the set of storage units, and sendsthe set of access requests to the set of storage units regarding the DSNaccess request, where the set of access requests includes indications ofparticular memory devices within the set of storage units, wherein theindications of particular memory devices are based on the one or moreproperties of the memory devices.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the dispersed storagenetwork or by other devices. In addition, at least one memory section(e.g., a non-transitory computer readable storage medium) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices of the dispersed storagenetwork (DSN), cause the one or more computing devices to perform any orall of the method steps described above.

FIG. 44A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1,and a plurality of storage sets 1-P. The DST processing unit 16 includesthe DST client module 34 of FIG. 1 and a decentralized agreement module460. The decentralized agreement module 460 may be implemented utilizingthe decentralized agreement module 350 of FIG. 40A. Hereafter, theplurality of storage sets may be interchangeably referred to as a DSNmemory. Each storage set includes a set of DST execution (EX) units 1-Nand a local area network (LAN), where each DST execution unit of the setof DST execution units is operably coupled to remaining DST executionunits of the set of DST execution units. Each DST execution unitincludes the processing module 84 of FIG. 3 and a set of memories 1-M.Each memory may be implemented utilizing the memory 88 of FIG. 3. EachDST execution unit may be implemented utilizing the DST execution unit36 of FIG. 1. Hereafter, each DST execution unit may be interchangeablyreferred to as a storage unit and a set of DST execution units may beinterchangeably referred to as a set of storage units and/or a storagepool.

The DSN functions to migrate encoded data slices of the DSN in responseto identified changes in the DSN memory, where the identified changes inthe DSN memory may include one or more of removing a storage set, addinga new storage set, removing a storage unit, adding a new storage unit,decommissioning a memory, and commissioning a new memory.

In an example of steps of operation of the migrating of the encoded dataslices, the DST client module 34 identifies the change in the DSN memoryof the DSN. The identifying includes one or more of receiving DSN memoryinformation 462 (e.g., an error message, a resource commissioningreport, a resource decommissioning report, active resource status, andconfiguration information), interpreting an error message, interpretinga commission report, interpreting a deactivation report, interpretingconfiguration information, initiating a query, and interpreting thereceived query response. For example, the DST client module 34 receivesDSN memory information 462 from DST execution unit 2 of the storage set1, where the DSN memory information 462 indicates that a memory M of DSTexecution unit 2 has been newly commissioned. As another example, theDST client module 34 receives further DSN memory information 462 fromthe DST execution unit 2, where the further DSN memory information 462indicates that memory 1 has been decommissioned.

Having detected the change in the DSN memory, the DST client module 34,for a set of encoded data slices of stored encoded data slices 464effected by the change in the DSN memory, where a data segment of a dataobject is dispersed storage error encoded to produce the set of encodeddata slices, ascertains updated properties of the DSN memory, where theupdated properties includes the change in the DSN memory (e.g., poolchange, storage unit change, memory change, etc.). For example, the DSTclient module 34 ascertains that the change in the DSN memory includesthe memory 1 of the DST execution unit 2 being decommissioned and thememory M of the DST execution unit 2 being newly commissioned.

The updated properties of the DSN memory may further include changes inweighting factors for one or more resource levels of the DSN memory. Forexample, the DST client module 34 ascertains that a weighting factorassociated with the memory 1 of the DST execution unit 2 beingdecommissioned is updated to zero and a weighting factor associated withthe memory M of the DST execution unit 2 being newly commissioned isestablished at a level associated with a newly established memory (e.g.,higher than average based on a higher than average available storagecapacity).

Having ascertained the updated properties of the DSN memory, the DSTclient module 34 performs an updating scoring function using one or moreproperties of DSN access information of the data segment (e.g., a DSNaddress) and one or more properties of the updated properties of the DSNmemory to produce an updated storage scoring resultant, where the DSNmemory includes the plurality of storage units that are logicallyarranged into the plurality of storage pools. As a specific example, theperforming of the updating scoring function includes the DST clientmodule 34 selecting a resource level (e.g., storage pool level, storageunit level, memory level) and selecting the one or more properties(e.g., associated weighting factors of the DSN resources associated withthe selected resource level) of the DSN memory from a plurality ofupdated properties of the DSN memory based on the selected resourcelevel. For instance, the DST client module 34 selects the memory levelwhen detecting the commissioning changes of the memories 1 and M of theDST execution unit 2 and selects location weights associated with theplurality of memories 1-M of the DST execution unit 2.

Having selected the resource level, the DST processing unit 16calculates, based on the selected resource level, a plurality of storagevalues based on the one or more properties of the DSN access informationand the one or more properties of the updated properties of the DSNmemory and performs a ranking function of the plurality of storagevalues to produce the updated storage scoring resultant. For example,the DST client module 34 issues a ranked scoring information request 466to the decentralized agreement module 460, where the ranked scoringinformation request 466 includes one or more of the one or moreproperties of the DSN access information of the data segment (e.g., aDSN address of the data segment), identifiers of a plurality resourcesassociated with the selected resource level (e.g., identifiers of thememories 1-M), and weighting factors of the plurality resourcesassociated with the selected resource level (e.g., updated weightingfactors for the memories 1-M).

Having received the ranked scoring information request 466, thedecentralized agreement module 460 performs a decentralized agreementprotocol function on the DSN address, the identifiers of the memories1-M, utilizing the weighting factors of the memories 1-M to produce theplurality of storage values associated with the memories 1-M for the setof encoded data slices. Having produced the plurality of storage values,the decentralized agreement module 460 performs the ranking function torank order the plurality of storage values associated with the memories1-M as the updated storage scoring resultant. Having produced theupdated storage scoring resultant, the decentralized agreement module460 issues ranked scoring information 468 to the DST client module 34,where the ranked scoring information 468 includes the updated storagescoring resultant. The generating of the updated storage scoringresultant is discussed in greater detail with reference to FIG. 44B.

Having received the ranked scoring information 468, the DST clientmodule 34 utilizes the updated storage scoring resultant to identify anupdated set of storage units of the plurality of storage unitsaffiliated with a given storage pool of the plurality of storage poolswhen the resource level includes the storage pool level. Alternatively,the DST client module 34 utilizes the updated storage scoring resultantto identify a memory of the plurality of memories 1-M of the DSTexecution unit 2 when the resource level includes the memory level.Further alternatively, the DST client module 34 utilizes the updatedstorage scoring resultant to identify another storage unit of the set ofstorage units when the resource level includes the storage unit level.

Having identified the updated set of storage units (e.g., anotherstorage pool when the resource level includes the storage pool level,another storage unit when the resource level includes the storage unitlevel, a storage unit associated with a plurality of memories when theresource level includes the memory level), the DST client module 34sends, via the network 24, at least one data migration request 470 to atleast one storage unit of the updated set of storage units regardingmigration of at least one encoded data slice of the set of encoded dataslices.

The sending of the at least one data migration request 470 to the atleast one storage unit includes facilitating one or more migrationalternatives. A first migration alternative includes migrating the setof encoded data slices as migration slices 472 from a set of storageunits in a second storage pool of the plurality of storage pools to theupdated set of storage units. A second migration alternative includesmigrating an encoded data slice as the migration slices 472 of the atleast one encoded data slice from a storage unit of an original set ofstorage units in the storage pool to a storage unit of the at least onestorage unit. A third migration alternative includes migrating anencoded data slice as the migration slices 472 of the at least oneencoded data slice from a first memory device of the at least onestorage unit to a second memory device of the at least one storage unit.For instance, the migration request 470 instructs the DST execution unit2 to migrate a slice 464 of the set of encoded data slices from thedecommissioned memory 1 to another memory (e.g., newly commissionedmemory M) of the plurality of memories of the DST execution unit 2 thatis associated with a highest storage value of the plurality of storagevalues of the updated storage scoring resultant.

A fourth migration alternative includes sending a data migration requestof the at least one data migration request 470 to a storage unit of theat least one storage unit and to another storage unit of the pluralityof storage units, where the other storage unit currently stores anencoded data slice of the at least one encoded data slice that is to bemigrated to the storage unit (e.g., same storage unit, but differentmemory within the storage unit; different storage units in same storagepool; different storage units in different pools). The DST client module34 may identify the other storage unit by performing a scoring functionusing the one or more properties of DSN access information of the datasegment and one or more non-updated properties of the DSN memory toproduce a storage scoring resultant (e.g., obtaining further rankedscoring information 468 from the decentralized agreement module 460associated with previous weighting factors of the DSN resources of theselected resource level). Having performed the scoring function, the DSTclient module 34 utilizes the storage scoring resultant to identify anon-updated set of storage units of the plurality of storage units(e.g., previous storage locations) and utilizes a particular property ofthe DSN access information (e.g., the DSN address) to identify the otherstorage unit.

Alternatively, a storage unit of the at least one storage unitdetermines a current storage location within the DSN memory of anencoded data slice of the at least one encoded data slice. As a specificexample, the storage unit performs the scoring function using the one ormore properties of DSN access information (e.g., the DSN address) of thedata segment and one or more non-updated properties (e.g., previousweighting factors) of the DSN memory to produce a storage scoringresultant (e.g., previous storage values), utilizes the storage scoringresultant to identify a non-updated set of storage units of theplurality of storage units (e.g., a previous storage set), and utilizesa particular property of the DSN access information to identify thecurrent storage location. The particular property enables identificationof the DSN address for the particular encoded data slice.

FIG. 44B is a schematic block diagram of another embodiment of adistributed storage and task (DST) processing unit 16 of FIG. 44A thatincludes the DST client module 34 of FIG. 44A and the decentralizedagreement module 460 of FIG. 44A. The DST client module 34 includes aweight module 480. The weight module 480 may be implemented utilizingthe processing module 84 of FIG. 3. The decentralized agreement module460 of FIG. 44B includes a plurality of deterministic functions 1-M, aplurality of normalizing functions 1-M, a plurality scoring functions1-M, and the ranking function 352 of FIG. 40A. Each deterministicfunction may be implemented utilizing the deterministic function of thedecentralized agreement module 350 of FIG. 40A. Each normalizingfunction may be implemented utilizing the normalizing function of thedecentralized agreement module 350 of FIG. 40A. Each scoring functionmay be implemented utilizing the scoring function of the decentralizedagreement module 350 of FIG. 40A.

FIG. 44B further illustrates the steps of the example of operation ofthe migrating of the encoded data slices of FIG. 44A where the DSTclient module 34 selects the memory level as the resource level. Havingselected the memory level, the weight module 480 of the DST clientmodule 34 receives the DSN memory information 462 and performs aweighting factor updating function on weighting factors of the DSNresources associated with the selected resource level to produce updatedweighting factors, where the weighting factor updating function is basedon the DSN memory information 462. For example, the weight module 480performs the weighting factor updating function on weighting factors ofthe memories 1-M in accordance with the DSN memory information 462(e.g., decommissioned memory 1, commission new memory M) to produceupdated weights for memories 1-M (e.g., zero out weighting factorassociated with decommissioned memory 1, establish a weighting factorassociated with newly commissioned memory M).

Having produced the updated weighting factors, the DST client module 34selects a memory identifier and a corresponding updated weighting factorfor each of the plurality of memories 1-M to produce a plurality ofmemory identifiers and a plurality of updated memory weighting factors,where the updated properties of DSN memory includes the plurality ofmemory identifiers and the plurality of updated memory weightingfactors. Having selected the identifiers and weighting factors, the DSTclient module 34 selects the DSN address 482 (e.g., source name) of theDSN access properties as the one or more properties of the DSN accessinformation. Having selected the DSN address 482, the DST client module34 issues a ranked scoring information request for memories 1-M todecentralized agreement module 460.

The decentralized agreement module 460 performs a series of functions onthe DSN address 482 based on the identifiers and the plurality ofweighting factors to produce a plurality of storage values (e.g., scores1-M). For example, for each memory, a deterministic function performs ahashing type deterministic function on an identifier of a combination ofa memory identifier concatenated with the DSN address 482 to produce aninterim result, the normalizing function performs the normalizingfunction on the interim result to produce a normalized interim result,the scoring function performs a scoring function on the normalizedinterim result and the weight associated with the memory to produce ascore of the plurality of storage values (e.g., scores 1-M).

Having produced the plurality of storage values, the decentralizedagreement module 460 performs a ranking function of the plurality ofstorage value to produce the storage scoring resultant. For example, theranking function 352 ranks scores associated with memories by score toproduce ranked scoring information for memories 1-M. For instance, theranking function 352 produces the storage scoring resultant thatindicates that newly commissioned memory M is associated with a highestscore. Having produced the storage scoring resultant, the decentralizedagreement module 460 sends the storage scoring resultant to the DSTclient module 34. Having received the storage scoring resultant, the DSTclient module 34 utilizes the storage scoring resultant to generate themigration requests 470 based on identifying a memory associated with ahighest storage value of the storage scoring resultant.

FIG. 44C is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 44A, the network 24 of FIG.44A, and the plurality of storage sets 1-P of FIG. 44A. The DSTprocessing unit 16 includes the DST client module 34 of FIG. 44A and thedecentralized agreement module 460 of FIG. 44A. Hereafter, the pluralityof storage sets may be interchangeably referred to as a DSN memory. Eachstorage set includes the set of DST execution (EX) units 1-N of FIG. 44Aand the local area network (LAN) of FIG. 44A, where each DST executionunit of the set of DST execution units is operably coupled to remainingDST execution units of the set of DST execution units. Each DSTexecution unit includes the processing module 84 of FIG. 44A and the setof memories 1-M of FIG. 44A. Hereafter, each DST execution unit may beinterchangeably referred to as a storage unit and a set of DST executionunits may be interchangeably referred to as a set of storage unitsand/or a storage pool.

The DSN functions to migrate encoded data slices of the DSN in responseto identified changes in the DSN memory, where the identified changes inthe DSN memory may include one or more of removing a storage set, addinga new storage set, removing a storage unit, adding a new storage unit,decommissioning a memory, and commissioning a new memory.

In an example of steps of operation of the migrating of the encoded dataslices, the DST client module 34 identifies the change in the DSN memoryof the DSN. The identifying includes one or more of receiving the DSNmemory information 462, interpreting an error message, interpreting acommission report, interpreting a deactivation report, interpretingconfiguration information, initiating a query, and interpreting thereceived query response. For example, the DST client module 34 receives,via the network 24, DSN memory information 462 from the DST executionunit 1 of the storage set 1, where the DSN memory information 462indicates that DST execution unit 1 has been decommissioned and the DSTclient module 34 receives, via the network 24, further DSN memoryinformation 462 from the DST execution unit N of the storage set 1,where the further DSN memory information 462 indicates that the DSTexecution unit N has been newly commissioned.

Having detected the change in the DSN memory, the DST client module 34,for a set of encoded data slices of stored encoded data slices 464effected by the change in the DSN memory (e.g., at least one encodeddata slices 464 stored in the memories 1-M of the DST execution unit 1),where a data segment of a data object is dispersed storage error encodedto produce the set of encoded data slices, ascertains updated propertiesof the DSN memory, where the updated properties includes the change inthe DSN memory (e.g., pool change, storage unit change, memory change,etc.). For example, the DST client module 34 ascertains that the changein the DSN memory includes the DST execution unit 1 being decommissionedand the DST execution unit N being newly commissioned.

The updated properties of the DSN memory may further include changes inweighting factors for one or more resource levels of the DSN memory. Forexample, the DST client module 34 ascertains that a weighting factorassociated with the DST execution unit 1 being decommissioned is updatedto zero and a weighting factor associated with the DST execution unit Nbeing newly commissioned is established at a level associated with anewly established storage unit (e.g., higher than average based on ahigher than average available storage capacity).

Having ascertained the updated properties of the DSN memory, the DSTclient module 34 performs an updating scoring function using one or moreproperties of DSN access information of the data segment (e.g., a DSNaddress, a slice name for a set of slices that includes an encoded dataslice 464 stored in the DST execution unit 1) and one or more propertiesof the updated properties of the DSN memory to produce an updatedstorage scoring resultant, where the DSN memory includes the pluralityof storage units that are logically arranged into the plurality ofstorage pools. The performing of the updating scoring function includesthe DST client module 34 selecting a resource level (e.g., storage poollevel, storage unit level, memory level) and selecting the one or moreproperties (e.g., associated weighting factors of the DSN resourcesassociated with the selected resource level) of the DSN memory from aplurality of updated properties of the DSN memory based on the selectedresource level. For the example, the DST client module 34 selects thestorage unit level when detecting the commissioning changes of the DSTexecution units 1 and N and selects location weighting factorsassociated with the set of DST execution units 1-N of the storage set 1.

Having selected the resource level, the DST processing unit 16calculates, based on the selected resource level, a plurality of storagevalues based on the one or more properties of the DSN access informationand the one or more properties of the updated properties of the DSNmemory and performs a ranking function of the plurality of storagevalues to produce the updated storage scoring resultant. For example,the DST client module 34 issues a ranked scoring information request 484to the decentralized agreement module 460, where the ranked scoringinformation request 484 includes one or more of the one or moreproperties of the DSN access information of the data segment (e.g., aDSN address of the data segment), identifiers of a plurality resourcesassociated with the selected resource level (e.g., identifiers of theDST execution units 1-N), and weighting factors of the pluralityresources associated with the selected resource level (e.g., updatedweighting factors for the DST execution units 1-N).

Having received the ranked scoring information request 484, thedecentralized agreement module 460 performs a decentralized agreementprotocol function on the DSN address, the identifiers of the DSTexecution units 1-N, utilizing the weighting factors of the DSTexecution units 1-N to produce the plurality of storage valuesassociated with the DST execution units 1-N for the set of encoded dataslices. Having produced the plurality of storage values, thedecentralized agreement module 460 performs the ranking function to rankorder the plurality of storage values associated with the DST executionunits 1-N as the updated storage scoring resultant. Having produced theupdated storage scoring resultant, the decentralized agreement module460 issues ranked scoring information 486 to the DST client module 34,where the ranked scoring information 486 includes the updated storagescoring resultant. The generating of the updated storage scoringresultant is discussed in greater detail with reference to FIG. 44D.

Having received the ranked scoring information 486, the DST clientmodule 34 utilizes the updated storage scoring resultant to identify anupdated set of storage units of the plurality of storage unitsaffiliated with a given storage pool of the plurality of storage poolswhen the resource level includes the storage pool level. Alternatively,the DST client module 34 utilizes the updated storage scoring resultantto identify another DST execution unit of the DST execution units 2-Nwhen the resource level includes the storage unit level and the DSTexecution unit 1 decommissioned (e.g., source of migration slices 490 totransfer to the other DST execution unit).

Having identified the updated set of storage units (e.g., anotherstorage pool when the resource level includes the storage pool level,another storage unit when the resource level includes the storage unitlevel, a storage unit associated with a plurality of memories when theresource level includes the memory level), the DST client module 34sends, via the network 24, at least one data migration request 488 to atleast one storage unit of the updated set of storage units regardingmigration of at least one encoded data slice of the set of encoded dataslices.

The sending of the at least one data migration request 488 to the atleast one storage unit includes facilitating one or more migrationalternatives. A first migration alternative includes migrating the setof encoded data slices as migration slices 490 from a set of storageunits in a second storage pool of the plurality of storage pools to theupdated set of storage units. A second migration alternative includesmigrating an encoded data slice as the migration slices 490 of the atleast one encoded data slice from a storage unit of an original set ofstorage units in the storage pool to a storage unit of the at least onestorage unit. A third migration alternative includes migrating anencoded data slice as the migration slices 490 of the at least oneencoded data slice from a first memory device of the at least onestorage unit to a second memory device of the at least one storage unit.

A fourth migration alternative includes sending a data migration requestof the at least one data migration request 488 to a storage unit of theat least one storage unit and to another storage unit of the pluralityof storage units, where the other storage unit currently stores anencoded data slice of the at least one encoded data slice that is to bemigrated to the storage unit (e.g., same storage unit, but differentmemory within the storage unit; different storage units in same storagepool; different storage units in different pools). The DST client module34 may identify the other storage unit by performing a scoring functionusing the one or more properties of DSN access information of the datasegment and one or more non-updated properties of the DSN memory toproduce a storage scoring resultant (e.g., obtaining further rankedscoring information 486 from the decentralized agreement module 460associated with previous weighting factors of the DSN resources of theselected resource level). Having performed the scoring function, the DSTclient module 34 utilizes the storage scoring resultant to identify anon-updated set of storage units of the plurality of storage units(e.g., previous storage locations) and utilizes a particular property ofthe DSN access information (e.g., the DSN address) to identify the otherstorage unit.

Alternatively, a storage unit of the at least one storage unitdetermines a current storage location within the DSN memory of anencoded data slice of the at least one encoded data slice. As a specificexample, the storage unit performs the scoring function using the one ormore properties of DSN access information (e.g., the DSN address) of thedata segment and one or more non-updated properties (e.g., previousweighting factors) of the DSN memory to produce a storage scoringresultant (e.g., previous storage values), utilizes the storage scoringresultant to identify a non-updated set of storage units of theplurality of storage units (e.g., a previous storage set), and utilizesa particular property of the DSN access information to identify thecurrent storage location. The particular property enables identificationof the DSN address for the particular encoded data slice.

FIG. 44D is a schematic block diagram of another embodiment of adistributed storage and task (DST) processing unit 16 of FIG. 44C thatincludes the DST client module 34 of FIG. 44C and the decentralizedagreement module 460 of FIG. 44C. The DST client module 34 includes theweight module 480 of FIG. 44B. The decentralized agreement module 460includes a plurality of deterministic functions 1-N, a plurality ofnormalizing functions 1-N, a plurality scoring functions 1-N, and theranking function 352 of FIG. 40A. Each deterministic function may beimplemented utilizing the deterministic function of the decentralizedagreement module 350 of FIG. 40A. Each normalizing function may beimplemented utilizing the normalizing function of the decentralizedagreement module 350 of FIG. 40A. Each scoring function may beimplemented utilizing the scoring function of the decentralizedagreement module 350 of FIG. 40A.

FIG. 44D further illustrates the steps of the example of operation ofthe migrating of the encoded data slices of FIG. 44C where the DSTclient module 34 selects the storage unit level as the resource level.Having selected the storage unit level, the weight module 480 of the DSTclient module 34 receives the DSN memory information 462 and performs aweighting factor updating function on weighting factors of the DSNresources associated with the selected resource level to produce updatedweighting factors, where the weighting factor updating function is basedon the DSN memory information 462. For example, the weight module 480performs the weighting factor updating function on weighting factors ofthe DST execution units 1-N of FIG. 44C in accordance with the DSNmemory information 462 (e.g., decommissioned DST execution unit 1,commission new DST execution unit N) to produce updated weights for theDST execution units 1-N (e.g., zero out weighting factor associated withdecommissioned DST execution unit 1, establish a weighting factorassociated with newly commissioned DST execution unit N).

Having produced the updated weighting factors, the DST client module 34selects a DST execution unit identifier and a corresponding updatedweighting factor for each of the set of DST execution units 1-N toproduce a plurality of storage units identifiers and a plurality ofupdated storage unit weighting factors, where the updated properties ofDSN memory includes the plurality of storage units identifiers and theplurality of updated storage unit weighting factors. Having selected theidentifiers and weighting factors, the DST client module 34 selects theDSN address 482 (e.g., source name) of the DSN access properties as theone or more properties of the DSN access information. Having selectedthe DSN address 482, the DST client module 34 issues a ranked scoringinformation request for storage units 1-N to the decentralized agreementmodule 460.

The decentralized agreement module 460 performs a series of functions onthe DSN address 482 based on the identifiers and the plurality ofweighting factors to produce a plurality of storage values (e.g., scores1-N). For example, for each storage unit, a deterministic functionperforms a hashing type deterministic function on an identifier of acombination of a storage unit identifier concatenated with the DSNaddress 482 to produce an interim result, the normalizing functionperforms the normalizing function on the interim result to produce anormalized interim result, the scoring function performs a scoringfunction on the normalized interim result and the weight associated withthe storage unit to produce a score of the plurality of storage values(e.g., scores 1-N).

Having produced the plurality of storage values, the decentralizedagreement module 460 performs a ranking function of the plurality ofstorage value to produce the storage scoring resultant. For example, theranking function 352 ranks scores associated with storage units by scoreto produce ranked scoring information for storage units 1-N. Forinstance, the ranking function 352 produces the storage scoringresultant that indicates that newly commissioned storage unit N isassociated with a highest score. Having produced the storage scoringresultant, the decentralized agreement module 460 sends the storagescoring resultant to the DST client module 34. Having received thestorage scoring resultant, the DST client module 34 utilizes the storagescoring resultant to generate the migration requests 488 based onidentifying a storage unit associated with a highest storage value ofthe storage scoring resultant.

FIG. 44E is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 44A, the network 24 of FIG.44A, the plurality of storage sets 1-P of FIG. 44A, and a newlycommissioned storage set P+1. The DST processing unit 16 includes theDST client module 34 of FIG. 44A and the decentralized agreement module460 of FIG. 44A. Hereafter, the plurality of storage sets may beinterchangeably referred to as a DSN memory. Each storage set includesthe set of DST execution (EX) units 1-N of FIG. 44A and the local areanetwork (LAN) of FIG. 44A, where each DST execution unit of the set ofDST execution units is operably coupled to remaining DST execution unitsof the set of DST execution units. Each DST execution unit includes theprocessing module 84 of FIG. 44A and the set of memories 1-M of FIG.44A. Hereafter, each DST execution unit may be interchangeably referredto as a storage unit and a set of DST execution units may beinterchangeably referred to as a set of storage units and/or a storagepool.

The DSN functions to migrate encoded data slices of the DSN in responseto identified changes in the DSN memory, where the identified changes inthe DSN memory may include one or more of removing a storage set, addinga new storage set, removing a storage unit, adding a new storage unit,decommissioning a memory, and commissioning a new memory.

In an example of steps of operation of the migrating of the encoded dataslices, the DST client module 34 identifies the change in the DSN memoryof the DSN. The identifying includes one or more of receiving the DSNmemory information 462, interpreting an error message, interpreting acommission report, interpreting a deactivation report, interpretingconfiguration information, initiating a query, and interpreting thereceived query response. For example, the DST client module 34 receives,via the network 24, DSN memory information 462 from at least one storageunit of the storage set 1, where the DSN memory information 462indicates that the storage set 1 has been decommissioned and the DSTclient module 34 receives, via the network 24, further DSN memoryinformation 462 from at least one storage unit of the storage set P+1,where the further DSN memory information 462 indicates that the storageset P+1 has been newly commissioned.

Having detected the change in the DSN memory, the DST client module 34,for a set of encoded data slices of stored encoded data slices 464effected by the change in the DSN memory (e.g., sets of encoded dataslices stored in the storage set 1) where a data segment of a dataobject is dispersed storage error encoded to produce the set of encodeddata slices, ascertains updated properties of the DSN memory, where theupdated properties includes the change in the DSN memory (e.g., poolchange, storage unit change, memory change, etc.). For example, the DSTclient module 34 ascertains that the change in the DSN memory includesthe storage set 1 being decommissioned and the storage set P+1 beingnewly commissioned.

The updated properties of the DSN memory may further include changes inweighting factors for one or more resource levels of the DSN memory. Forexample, the DST client module 34 ascertains that a weighting factorassociated with the storage set 1 being decommissioned is updated tozero and a weighting factor associated with the storage set P+1 beingnewly commissioned is established at a level associated with a newlyestablished storage set (e.g., higher than average based on a higherthan average available storage capacity).

Having ascertained the updated properties of the DSN memory, the DSTclient module 34 performs an updating scoring function using one or moreproperties of DSN access information of the data segment (e.g., a DSNaddress, a source name for a plurality of sets of encoded data slicesthat includes encoded data slices 464 stored in the storage set 1) andone or more properties of the updated properties of the DSN memory toproduce an updated storage scoring resultant, where the DSN memoryincludes the plurality of storage units that are logically arranged intothe plurality of storage pools. The performing of the updating scoringfunction includes the DST client module 34 selecting a resource level(e.g., storage pool level, storage unit level, memory level) andselecting the one or more properties (e.g., associated weighting factorsof the DSN resources associated with the selected resource level) of theDSN memory from a plurality of updated properties of the DSN memorybased on the selected resource level. For the example, the DST clientmodule 34 selects the storage set level when detecting the commissioningchanges of the storage sets 1 and P+1 and selects location weightingfactors associated with the plurality of storage sets 1 through P+1.

Having selected the resource level, the DST processing unit 16calculates, based on the selected resource level, a plurality of storagevalues based on the one or more properties of the DSN access informationand the one or more properties of the updated properties of the DSNmemory and performs a ranking function of the plurality of storagevalues to produce the updated storage scoring resultant. For example,the DST client module 34 issues a ranked scoring information request 492to the decentralized agreement module 460, where the ranked scoringinformation request 492 includes one or more of the one or moreproperties of the DSN access information of the data segment (e.g., aDSN address of the data segment), identifiers of a plurality resourcesassociated with the selected resource level (e.g., identifiers of thestorage sets 1 through P+1), and weighting factors of the pluralityresources associated with the selected resource level (e.g., updatedweighting factors for the storage sets 1 through P+1).

Having received the ranked scoring information request 492, thedecentralized agreement module 460 performs a decentralized agreementprotocol function on the DSN address, the identifiers of the storagesets 1 through P+1, utilizing the weighting factors of the storage sets1 through P+1 to produce the plurality of storage values associated withthe storage sets 1 through P+1 for the set of encoded data slices.Having produced the plurality of storage values, the decentralizedagreement module 460 performs the ranking function to rank order theplurality of storage values associated with the storage sets 1 throughP+1 as the updated storage scoring resultant. Having produced theupdated storage scoring resultant, the decentralized agreement module460 issues ranked scoring information 494 to the DST client module 34,where the ranked scoring information 494 includes the updated storagescoring resultant. The generating of the updated storage scoringresultant is discussed in greater detail with reference to FIG. 44F.

Having received the ranked scoring information 494, the DST clientmodule 34 utilizes the updated storage scoring resultant to identify anupdated set of storage units of the plurality of storage unitsaffiliated with a given storage pool of the plurality of storage poolswhen the resource level includes the storage pool level. For example,the DST client module 34 identifies, for a given source name, thestorage set P+1 for transfer of encoded data slices 464 from the storageset 1. Having identified the updated set of storage units (e.g., anotherstorage pool when the resource level includes the storage pool level,another storage unit when the resource level includes the storage unitlevel, a storage unit associated with a plurality of memories when theresource level includes the memory level), the DST client module 34sends, via the network 24, at least one data migration request 496 to atleast one storage unit of the updated set of storage units regardingmigration of at least one encoded data slice of the set of encoded dataslices.

The sending of the at least one data migration request 496 to the atleast one storage unit includes facilitating one or more migrationalternatives. A first migration alternative includes migrating the setof encoded data slices as migration slices 498 from a set of storageunits in a second storage pool of the plurality of storage pools to theupdated set of storage units (e.g., migrating slices 464 from storageset 1 to storage set P+1). A second migration alternative includesmigrating an encoded data slice as the migration slices 498 of the atleast one encoded data slice from a storage unit of an original set ofstorage units in the storage pool to a storage unit of the at least onestorage unit. A third migration alternative includes migrating anencoded data slice as the migration slices 498 of the at least oneencoded data slice from a first memory device of the at least onestorage unit to a second memory device of the at least one storage unit.

A fourth migration alternative includes sending a data migration requestof the at least one data migration request 496 to a storage unit of theat least one storage unit and to another storage unit of the pluralityof storage units, where the other storage unit currently stores anencoded data slice of the at least one encoded data slice that is to bemigrated to the storage unit (e.g., same storage unit, but differentmemory within the storage unit; different storage units in same storagepool; different storage units in different pools). The DST client module34 may identify the other storage unit by performing a scoring functionusing the one or more properties of DSN access information of the datasegment and one or more non-updated properties of the DSN memory toproduce a storage scoring resultant (e.g., obtaining further rankedscoring information 494 from the decentralized agreement module 460associated with previous weighting factors of the DSN resources of theselected resource level). Having performed the scoring function, the DSTclient module 34 utilizes the storage scoring resultant to identify anon-updated set of storage units of the plurality of storage units(e.g., previous storage locations) and utilizes a particular property ofthe DSN access information (e.g., the DSN address) to identify the otherstorage unit.

Alternatively, a storage unit of the at least one storage unitdetermines a current storage location within the DSN memory of anencoded data slice of the at least one encoded data slice. As a specificexample, the storage unit performs the scoring function using the one ormore properties of DSN access information (e.g., the DSN address) of thedata segment and one or more non-updated properties (e.g., previousweighting factors) of the DSN memory to produce a storage scoringresultant (e.g., previous storage values), utilizes the storage scoringresultant to identify a non-updated set of storage units of theplurality of storage units (e.g., a previous storage set), and utilizesa particular property of the DSN access information to identify thecurrent storage location. The particular property enables identificationof the DSN address for the particular encoded data slice.

FIG. 44F is a schematic block diagram of another embodiment of adistributed storage and task (DST) processing unit 16 of FIG. 44E thatincludes the DST client module 34 of FIG. 44E and the decentralizedagreement module 460 of FIG. 44E. The DST client module 34 includes theweight module 480 of FIG. 44B. The decentralized agreement module 460includes a plurality of deterministic functions 1-P+1, a plurality ofnormalizing functions 1-P+1, a plurality scoring functions 1-P+1, andthe ranking function 352 of FIG. 40A. Each deterministic function may beimplemented utilizing the deterministic function of the decentralizedagreement module 350 of FIG. 40A. Each normalizing function may beimplemented utilizing the normalizing function of the decentralizedagreement module 350 of FIG. 40A. Each scoring function may beimplemented utilizing the scoring function of the decentralizedagreement module 350 of FIG. 40A.

FIG. 44F further illustrates the steps of the example of operation ofthe migrating of the encoded data slices of FIG. 44E where the DSTclient module 34 selects the storage pool (e.g., storage set) level asthe resource level. Having selected the storage pool level, the weightmodule 480 of the DST client module 34 receives the DSN memoryinformation 462 and performs a weighting factor updating function onweighting factors of the DSN resources associated with the selectedresource level to produce updated weighting factors, where the weightingfactor updating function is based on the DSN memory information 462. Forexample, the weight module 480 performs the weighting factor updatingfunction on weighting factors of the storage sets 1 through P+1 of FIG.44E in accordance with the DSN memory information 462 (e.g.,decommissioned storage set 1, commission new set P+1) to produce updatedweights for the storage sets 1 through P+1 (e.g., zero out weightingfactor associated with decommissioned storage set 1, establish aweighting factor associated with newly commissioned storage set P+1).

Having produced the updated weighting factors, the DST client module 34selects a storage set identifier and a corresponding updated weightingfactor for each of the storage sets 1 through P+1 to produce a pluralityof storage set identifiers and a plurality of updated storage setweighting factors, where the updated properties of DSN memory includesthe plurality of storage set identifiers and the plurality of updatedstorage set weighting factors. Having selected the identifiers andweighting factors, the DST client module 34 selects the DSN address 482(e.g., source name) of the DSN access properties as the one or moreproperties of the DSN access information. Having selected the DSNaddress 482, the DST client module 34 issues a ranked scoringinformation request for storage sets 1 through P+1 to the decentralizedagreement module 460.

The decentralized agreement module 460 performs a series of functions onthe DSN address 482 based on the identifiers and the plurality ofweighting factors to produce a plurality of storage values (e.g., scores1 through P+1). For example, for each storage set, a deterministicfunction performs a hashing type deterministic function on an identifierof a combination of a storage set identifier concatenated with the DSNaddress 482 to produce an interim result, the normalizing functionperforms the normalizing function on the interim result to produce anormalized interim result, the scoring function performs a scoringfunction on the normalized interim result and the weight associated withthe storage unit to produce a score of the plurality of storage values(e.g., scores 1 through P+1).

Having produced the plurality of storage values, the decentralizedagreement module 460 performs a ranking function of the plurality ofstorage value to produce the storage scoring resultant. For example, theranking function 352 ranks scores associated with storage sets by scoreto produce ranked scoring information for storage sets 1 through P+1.For instance, the ranking function 352 produces the storage scoringresultant that indicates that newly commissioned storage set P+1 isassociated with a highest score. Having produced the storage scoringresultant, the decentralized agreement module 460 sends the storagescoring resultant to the DST client module 34. Having received thestorage scoring resultant, the DST client module 34 utilizes the storagescoring resultant to generate the migration requests 496 based onidentifying a storage set associated with a highest storage value of thestorage scoring resultant.

FIG. 44G is a flowchart illustrating an example of migrating encodeddata slices in a dispersed storage network (DSN) memory. In particular,a method is presented for use in conjunction with one or more functionsand features described in conjunction with FIGS. 1-39, 44A-F, and alsoFIG. 44G. The method begins at step 500 where a processing module of acomputing device of one or more computing devices of a dispersed storagenetwork (DSN) identifies a change in DSN memory of the DSN. The methodcontinues at step 502 where the processing module, for a set of encodeddata slices effected by the change in the DSN memory, where a datasegment of a data object is dispersed storage error encoded to producethe set of encoded data slices, ascertains updated properties of the DSNmemory, where the updated properties includes the change in the DSNmemory.

The method continues at step 504 where the processing module performs anupdating scoring function using one or more properties of DSN accessinformation (e.g., a DSN address) of the data segment and one or moreproperties of the updated properties of the DSN memory to produce anupdated storage scoring resultant, where the DSN memory includes aplurality of storage units that are logically arranged into a pluralityof storage pools. The performing of the updating scoring function mayfurther include selecting a resource level and selecting the one or moreproperties of the DSN memory from a plurality of updated properties ofthe DSN memory based on the selected resource level. Having selected theresource level, the processing module calculates, based on the selectedresource level, a plurality of storage values based on the one or moreproperties of the DSN access information and the one or more propertiesof the updated properties of the DSN memory, and performs a rankingfunction of the plurality of storage values to produce the updatedstorage scoring resultant.

The method continues at step 506 where the processing module utilizesthe updated storage scoring resultant to identify an updated set ofstorage units of the plurality of storage units affiliated with a givenstorage pool of the plurality of storage pools. The method continues atstep 508 where the processing module sends at least one data migrationrequest to at least one storage unit of the updated set of storage unitsregarding migration of at least one encoded data slice of the set ofencoded data slices.

The sending the at least one data migration request to the at least onestorage unit includes one or more migration alternatives. A firstmigration alternatives includes migrating the set of encoded data slicesfrom a set of storage units in a second storage pool of the plurality ofstorage pools to the updated set of storage units. A second migrationalternatives includes migrating an encoded data slice of the at leastone encoded data slice from a storage unit of an original set of storageunits in the storage pool to a storage unit of the at least one storageunit. A third migration alternatives includes migrating an encoded dataslice of the at least one encoded data slice from a first memory deviceof the at least one storage unit to a second memory device of the atleast one storage unit. A fourth migration alternatives includes sendinga data migration request of the at least one data migration request to astorage unit of the at least one storage unit and to another storageunit of the plurality of storage units, where the other storage unitcurrently stores an encoded data slice of the at least one encoded dataslice that is to be migrated to the storage unit.

The identifying the other storage unit may include one or more ofperforming a scoring function using the one or more properties of DSNaccess information of the data segment and one or more non-updatedproperties of the DSN memory to produce a storage scoring resultant,utilizing the storage scoring resultant to identify a non-updated set ofstorage units of the plurality of storage units, and utilizing aparticular property of the DSN access information to identify the otherstorage unit.

Alternatively, or in addition to, the storage unit of the at least onestorage unit determines a current storage location within the DSN memoryof an encoded data slice of the at least one encoded data slice by oneor more of performing a scoring function using the one or moreproperties of DSN access information of the data segment and one or morenon-updated properties of the DSN memory to produce a storage scoringresultant, utilizing the storage scoring resultant to identify anon-updated set of storage units of the plurality of storage units, andutilizing a particular property of the DSN access information toidentify the current storage location.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the dispersed storagenetwork or by other devices. In addition, at least one memory section(e.g., a non-transitory computer readable storage medium) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices of the dispersed storagenetwork (DSN), cause the one or more computing devices to perform any orall of the method steps described above.

FIG. 45A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes a distributed storage andtask (DST) processing unit 16, the network 24 of FIG. 1, and adistributed storage and task network (DSTN) module 22. The DSTprocessing module 16 includes the DST client module 34 of FIG. 1 and adecentralized agreement module 570. The decentralized agreement module570 may be implemented utilizing the decentralized agreement module 350of FIG. 40A. The DSTN module 22 includes a plurality of DST execution(EX) unit pools 1-P. Each DST execution unit pool includes a set of DSTexecution units 1-N. Each DST execution unit may be implementedutilizing the DST execution unit 36 of FIG. 1. Hereafter, each DSTexecution unit may be interchangeably referred to as a storage unit.

The DSN functions to access data stored in one or more of the DSTexecution unit pools and to migrate encoded data slice assets from afirst DST execution unit pool to a second DST execution unit pool. TheDST client module 34 dispersed storage error encodes the data to producea plurality of sets of encoded data slices for storage in the set of DSTexecution units of the DST execution unit pool 1. For example, encodeddata slices 1-1 through N-1 corresponding to a first set of encoded dataslices are stored in the DST execution units 1-N of the DST executionunit pool 1, encoded data slices 1-2 through N-2 corresponding to asecond set of encoded data slices are stored in the DST execution units1-N of the DST execution unit pool 1, etc.

In an example of operation, the plurality of sets of encoded data slicesstored in the DST execution units 1-N of the DST execution unit pool 1are in the process of being migrated to the DST execution units 1-N ofthe DST execution unit pool 2. For example, the DST client module 34issues migration requests 580 to the DST execution units of the DSTexecution unit pools 1 and 2 and receives migration responses 582 inaccordance with progress of migration. For instance, the progress ofmigration, at a given point in time during the migration, indicates thatall N encoded data slices of the first set of encoded data slices havebeen migrated, only encoded data slice 2-2 of the second set of encodeddata slices has been migrated, and none of the encoded data slices of athird set of encoded data slices have been migrated.

During the migration, a DST execution unit receives a resource accessrequest 572, where the resource access request 572 includes a read slicerequest for an encoded data slice. The DST execution unit determineswhether the DST execution unit is associated with the encoded dataslice. For example, the DST execution unit performs a decentralizedagreement function using a slice name associated with encoded dataslice, location IDs associated with other DST execution units, andutilizing current location weights associated with the DST executionunits to produce ranked scoring information 578. The DST execution unitindicates an affirmative association when a highest score of the rankedscoring information is associated with the DST execution unit.Alternatively, the DST client module 34 facilitates performing of thedecentralized agreement function by the decentralized agreement module570 by issuing a ranked scoring information request 576 to thedecentralized agreement module 570 and receiving the ranked scoringinformation 578 in response.

When the DST execution unit is associated with encoded data slice, theDST execution unit issues a resource access response 574 that includesthe encoded data slice when the encoded data slices available from amemory of the DST execution unit. When the DST execution unit isassociated with the encoded data slice and the encoded data slice isunavailable from memory of the DST execution unit, the DST executionunit identifies a previous DST execution unit associated with encodeddata slice. For example, the DST execution unit performs thedecentralized agreement function utilizing a previous location weightsto produce previous ranked scoring information. Alternatively, the DSTexecution unit retrieves previous ranked scoring information to identifya DST execution unit associated with a highest score of the previousranked scoring information as the previous DST execution unit.

Having identified the previous DST execution unit, the DST executionunit obtains the encoded data slice from the previous DST executionunit. For example, the DST execution unit issues a read slice requestthat includes the slice name to the previous DST execution unit andreceives the encoded data slice. Having received the encoded data slice,the DST execution unit stores the received encoded data slice in a localmemory of the DST execution unit. Having stored the received encodeddata slice, the DST execution unit issues a resource access response 574that includes the encoded data slice.

FIG. 45B is a flowchart illustrating an example of recovering a storedencoded data slice. The method begins or continues at step 584 where aprocessing module (e.g., of a storage unit) receives, by the storageunit, a read slice request for an encoded data slice. For example, theprocessing module extracts a slice name associated with encoded dataslice from the read slice requests. The method continues at step 586where the processing module determines whether the storage unit isassociated with the encoded data slice. For example, the processingmodule performs a decentralized agreement function using the slice nameas an asset identifier with regards to the storage unit and otherstorage units of a dispersed storage network memory utilizing currentlocation weights to produce ranked scoring information. The processingmodule indicates an association when a highest score is associated withthe storage unit.

When the storage unit is associated with the encoded data slice andencoded data slices available from the memory of the storage unit, themethod continues at step 588 where the processing module issues a readslice response that includes the encoded data slice. For example, theprocessing module generates a read slice response that includes theencoded data slice and sends the read slice response to a requestingentity.

When the storage unit associated with the encoded data slice and theencoded data slice is unavailable from the memory of the storage unit,the method continues at step 590 where the processing module identifiesa previous storage unit associated with the encoded data slice. Forexample, the processing module performs the decentralized agreementfunction using the slice name with regards to the storage unit and theother storage units of the DSN memory utilizing a previous locationweights to produce previous ranked scoring information. The processingmodule identifies another storage unit associated with a highest scoreof the previous ranked scoring information as the previous storage unit.

The method continues at step 592 where the processing module obtains theencoded data slice from the previous storage unit. For example, theprocessing module generates a read slice requests that includes theslice name of the encoded data slice, sends the read slice request tothe previous storage unit, receives a read slice response that includesthe encoded data slice, and stores the encoded data slice in the memoryof the storage unit.

The method continues at step 594 where the processing module issues aread slice response that includes the encoded data slice. For example,the processing module generates a read slice response that includesencoded data slice and sends the read slice response to the requestingentity.

FIG. 46A is a schematic block diagram of another embodiment of adecentralized agreement module 596 that includes a plurality ofdeterministic functions, the de-normalizing functions of FIG. 40A, thescoring functions of FIG. 40A, and the ranking function 352 of FIG. 40A.Each deterministic function includes a range module, a combining module,and a deterministic function module. The decentralized agreement module596 receives the ranked scoring information request 354 of FIG. 40A andgenerates the ranked scoring information 358 of FIG. 40A.

In an example of operation, the decentralized agreement module 596receives the ranked scoring information request 354 from a requestingentity. Having received the ranked scoring information request 354, therange module converts an asset identifier (ID) 356 of the ranked scoringinformation request 354 into a range identifier 598. The convertingincludes at least one of deleting one or more least significant bytes,performing a lookup, and performing an alternate deterministic functionwhere a number of bits of the range identifier is less than a number ofbits of the asset ID. For example, range module 2 deletes 4 leastsignificant bytes of the asset ID to produce the range ID.

For each location ID associated with the range identifier 598, eachcombining module combines the range ID 598 and a corresponding locationID to produce a corresponding combined value. For example, combiningmodule 2 combines the range ID and location ID 2 to produce combinedvalue 2. The deterministic function module performs a deterministicfunction on the combined value to produce a corresponding interimresult. For example, deterministic function module 2 performs thedeterministic function on the combined value 2 to produce an interimresult 2.

For each interim result, each normalizing function performs anormalizing function on the interim result to produce a correspondingnormalizing to result. For each normalized interim result, each scoringfunction performs a scoring function on a corresponding normalizedinterim result using the location weight associated with the location IDassociated with the normalized interim result to produce a score of aset of scores. The ranking function 352 orders the set of scores byassociated location ID to produce the ranked scoring information 358 foroutput to the requesting entity.

FIG. 46B is a flowchart illustrating another example of selecting aresource, that includes similar steps to FIG. 40B. The method begins orcontinues with step 360 of FIG. 40B where a processing module (e.g., ofa decentralized agreement module) receives a ranked scoring informationrequest from a requesting entity with regards to a set of candidateresources. The method continues at step 600 where the processing moduleconverts an asset identifier (ID) of the ranked scoring informationrequest into a range identifier (ID). For example, the processing modulereduces the number of bits of the asset ID in accordance with areduction approach to produce the range identifier. In an instance ofthe reducing, the processing module identifies an asset ID type (e.g.,source name, slice name), determines the reduction approach based on theasset ID type (e.g., delete a least significant two bytes for a sourcename type, delete a segment number field for a slice name type), andperforms the reduction based on the reduction approach.

For each candidate resource, the method continues at step 602 where theprocessing module performs a deterministic function on a location ID ofthe candidate resource and the range identifier to produce an interimresult. For example, the processing module combines and/or concatenatesthe range identifier and the location ID of the candidate resource toproduce a combined value and performs the deterministic function on thecombined value to produce the interim result. The method continues withstep 364-370 of FIG. 40B where, for each interim result, the processingmodule performs a normalizing function on the interim result to producea normalized interim result; for each normalized interim result,performs a scoring function on the normalized interim result utilizing alocation weight associated with a candidate resource associated with theinterim result to produce a score of a set of scores; rank orders theset of scores to produce ranked scoring information; and outputs therank scoring information to the requesting entity.

FIG. 47A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes a distributed storage andtask (DST) processing unit 16, the network 24 of FIG. 1, and thedistributed storage and task network (DSTN) module 22 of FIG. 45A. TheDST processing unit 16 includes the DST client module 34 of FIG. 1 and adecentralized agreement module 604. The decentralized agreement module604 may be implemented utilizing the decentralized agreement module 350of FIG. 40A. The DSN functions to access data stored in the DSTN module22 in accordance with a decentralized agreement function.

In an example of operation, the DST client module 34 determines toretrieve an encoded data slice associated with a slice name (e.g.,receiving a request, identify required data). The DST client module 34obtains DSN configuration information 606. The obtaining includes atleast one of retrieving from a local memory, recovering from the DSTNmodule 22, and receiving. The DSN configuration information 606 includesinformation with regards to modifications to a DSN configuration. Themodifications includes one or more of current resources and locationweights, previous resources and previous location weights, utilizationhistory resources, and migration cursor DSN address value for activeslice migrations.

Having obtained the DSN configuration information 606, the DST clientmodule 34 identifies one or more configurations of the DSN configurationinformation 606, where the one or more configurations include a currentconfiguration. Each configuration includes a list of resources andassociated location weights. For each configuration, the DST clientmodule 34 determines ranked scoring information 610 for a plurality ofresources of the configuration with regards to the slice name as anasset ID. For example, the DST client module 34 issues a ranked scoringinformation request 608 to the decentralized agreement module 604 andreceives the ranked scoring information 610, where the ranked scoringinformation request 608 includes the asset ID and the DSN configurationinformation.

For each configuration, the DST client module 34 selects a resourcebased on associated ranked scoring information 610 for the pluralityresources of the configuration, where the plurality of resourcesincludes the selected resource (e.g., selected resource is associatedwith a highest score). Having selected the resource, the DST clientmodule 34 determines a likelihood level for each selected resource ofeach configuration. The determining includes calculating a probabilitythat the encoded data slice is retrievable from the selectedresource-based on one or more of a migration status, resourceavailability indicator, and a resource utilization history.

Having determined the likelihood level for each selected resource, theDST client module 34 identifies one or more of the selected resourcesfor slice retrieval based on the likelihood level for each selectedresource. For example, the DST client module 34 selects a first m numberof resources associated with a highest probabilities. As anotherexample, the DST client module 34 selects those resources associatedwith probabilities greater than a probability threshold level.

Having identified the one or more of the selected resources for sliceretrieval, the DST client module 34 issues read slice requests asresource access requests 612 to the identified one or more selectedresources requesting retrieval of the encoded data slice. The DST clientmodule 34 receives at least one resource access response 614 thatincludes at least one read slice response, where the at least one readslice response includes encoded data slice.

FIG. 47B is a flowchart illustrating another example of selecting aresource. The method begins or continues at step 616 where a processingmodule (e.g., of a distributed storage and task (DST) client module)determines to retrieve an encoded data slice from a dispersed storagenetwork (DSN) memory that includes a plurality of resources (e.g., aplurality of storage units). The determining includes one or more ofreceiving a request, identifying required data, and performing a lookupof a DSN address of the encoded data slice.

The method continues at step 618 where the processing module obtains DSNconfiguration information. The obtaining the DSN configurationinformation includes at least one of recovering from the DSN memory,receiving, and retrieving from a local memory. The method continues atstep 620 where the processing module identifies one or moreconfigurations of the DSN configuration information. The identifyingincludes at least one of extracting a current configuration, extractingprevious configurations, and extracting resource utilization history.

For each configuration, the method continues at step 622 where theprocessing module determines ranked scoring information for a subset ofthe plurality of resources associated with the configuration withregards to the encoded data slice. The determining includes utilizing adecentralized agreement function based on configuration information ofthe configuration and a slice name of the encoded data slice. For eachconfiguration, the method continues at step 624 where the processingmodule selects a resource based on associated ranked scoring informationfor the subset of the plurality of resources. For example, theprocessing module identifies a resource associated with a highest scoreof the rank scoring information. As another example, the processingmodule identifies more than one resource by identifying a secondresource with a next highest score.

The method continues at step 626 where the processing module determinesa likelihood level for each selected resource of each configuration. Forexample, the processing module calculates a retrieval probability levelfrom the selected resource. The method continues at step 628 where theprocessing module identifies one or more of the selected resources forslice retrieval based on the likelihood level of each selected resource.The identifying may be in accordance with a selection method. Forexample, the processing module selects m number of top-ranked resources.As another example, the processing module selects those resources with alikelihood level greater than a likelihood threshold level.

The method continues at step 630 where the processing module issues readslice requests to the identified one or more selected resources for theencoded data slice. For example, the processing module generates theread slice request to include the slice name and sends the read slicerequest to each of the identified one or more selected resources. Themethod continues at step 632 where the processing module receivesencoded data slice from at least one of the identified one or moreselected resources.

FIG. 48A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes a load-balancing module634, a distributed storage and task (DST) processing unit pool 636, thenetwork 24 of FIG. 1, and the DSTN module 22 of FIG. 1. Theload-balancing module 634 includes a decentralized agreement module 638and the DST client module 34 of FIG. 1. The load-balancing module 634may be implemented utilizing the DST processing unit 16 of FIG. 1. Thedecentralized agreement module 638 may be implemented utilizing thedecentralized agreement module 350 of FIG. 40A. The DST processing unitpool 636 includes a plurality of DST processing units 1-P. Each DSTprocessing unit may be implemented utilizing the DST processing unit 16of FIG. 1.

The DSN functions to access data stored in the DSTN module 22. Theaccessing of the data utilizes a decentralized agreement function toselect at least one DST processing unit of the DST processing unit poolto facilitate the accessing of the data. In an example of operation, theDST client module 34 receives a data access request 640 regarding thedata stored in the DSTN module 22. The data access request 640 includesa data name associated with the data. Having received the data accessrequest 640, the DST client module 34 determines DSN addressinginformation of the data access request. The DSN addressing informationincludes at least one of a source name range, a source name, and a slicename. The determining may be based on one or more of generating when thedata access request includes a store data request and performing alookup utilizing the data name when the data access request includes aretrieve data request.

Having determined the DSN addressing information, the DST client module34 obtains DSN configuration information 650 that includes accessresource configuration information for a plurality of access resources(e.g., DST processing units) with regards to the DSN memory of the DSTNmodule 22. The DSN configuration information 650 includes one or more ofcapacity for each access resource, identifiers of the access resources(e.g., individual identifiers of the plurality of DST processing units,an identifier for the pool), and a current loading level of the accessresources. The obtaining includes at least one of initiating a query,receiving a query response, performing a lookup, generating, andreceiving.

Having obtained the DSN configuration information 650, the DST clientmodule 34 determines ranked scoring information 644 for the plurality ofaccess resources based on the DSN configuration information 650 and thedata access request 640. For example, the DST client module 34 issues aranked scoring information request 642 to the decentralized agreementmodule 638 and receives the ranked scoring information 644, where eachscore is calculated as a processing capacity of a resource divided by anegative log of a normalized deterministic function value based on acombination of one or more of a DST processing unit pool identifier, andindividual DST processing unit identifier, and the source name as theDSN addressing information.

Having determined the ranked scoring information 644, the DST clientmodule 34 identifies an access resource of the plurality of accessresources based on the ranked scoring information 644 to produce aselected access resource. The identifying includes selecting a DSTprocessing unit associated with a highest score of the ranked scoringinformation 644. Having identified the access resource, the DST clientmodule 34 forwards the data access request as a data access message 646to the selected access resource, where the selected access resourceaccesses the DSTN module 22 by issuing a slice access messages 648,receiving other slice access messages section 648, and issuing furtherdata access messages 646 based on received slice access messages 648.Having received the data access messages 646, the DST client module 34issues a data access response 652 to a requesting entity based on thereceived data access messages 646. Alternatively, or in addition to, theselected access resource issues the data access response 652 directly tothe requesting entity.

FIG. 48B is a flowchart illustrating an example of selecting an accessresource. The method begins or continues at step 654 where a processingmodule (e.g., of a distributed storage and task (DST) client module)receives a data access request from a requesting entity. The data accessrequest includes a data identifier and a requesting entity identifier.The method continues at step 656 where the processing module determinesdispersed storage network (DSN) addressing information of the dataaccess request. The determining includes at least one of generating andperforming a lookup using the data identifier.

The method continues at step 658 where the processing module obtains DSNconfiguration information with regards to a plurality of accessresources. For example, the processing module recovers the DSNconfiguration information from DSN memory. As another example, theprocessing module accesses a local file. As yet another example, theprocessing module extracts the DSN configuration information from thedata access request.

The method continues at step 660 where the processing module determinesranked scoring information for the plurality of access resources basedon the DSN addressing information and the data access request. Forexample, the processing module performs a decentralized agreementfunction on identifiers of the plurality of access resources, the DSNaddressing information, and a capability level of each access resourceto produce the ranked scoring information.

The method continues at step 662 where the processing module selects anaccess resource of the plurality of access resources based on the rankedscoring information. For example, the processing module selects anaccess resource associated with a highest score of the ranked scoringinformation. The method continues at step 664 where the processingmodule facilitates processing of the data access request by the selectedaccess resource. For example, the processing module sends the dataaccess request to the selected access resource, receives a data accessresponse, and forwards the received data access response to therequesting entity.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “operably coupled to”, “coupled to”, and/or “coupling” includesdirect coupling between items and/or indirect coupling between items viaan intervening item (e.g., an item includes, but is not limited to, acomponent, an element, a circuit, and/or a module) where, for indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.As may even further be used herein, the term “operable to” or “operablycoupled to” indicates that an item includes one or more of powerconnections, input(s), output(s), etc., to perform, when activated, oneor more its corresponding functions and may further include inferredcoupling to one or more other items. As may still further be usedherein, the term “associated with”, includes direct and/or indirectcoupling of separate items and/or one item being embedded within anotheritem. As may be used herein, the term “compares favorably”, indicatesthat a comparison between two or more items, signals, etc., provides adesired relationship. For example, when the desired relationship is thatsignal 1 has a greater magnitude than signal 2, a favorable comparisonmay be achieved when the magnitude of signal 1 is greater than that ofsignal 2 or when the magnitude of signal 2 is less than that of signal1.

As may also be used herein, the terms “processing module”, “processingcircuit”, and/or “processing unit” may be a single processing device ora plurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module, module, processingcircuit, and/or processing unit may be, or further include, memoryand/or an integrated memory element, which may be a single memorydevice, a plurality of memory devices, and/or embedded circuitry ofanother processing module, module, processing circuit, and/or processingunit. Such a memory device may be a read-only memory, random accessmemory, volatile memory, non-volatile memory, static memory, dynamicmemory, flash memory, cache memory, and/or any device that storesdigital information. Note that if the processing module, module,processing circuit, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

The present invention has been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claimed invention. Further, theboundaries of these functional building blocks have been arbitrarilydefined for convenience of description. Alternate boundaries could bedefined as long as the certain significant functions are appropriatelyperformed. Similarly, flow diagram blocks may also have been arbitrarilydefined herein to illustrate certain significant functionality. To theextent used, the flow diagram block boundaries and sequence could havebeen defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claimed invention. One of average skill in the artwill also recognize that the functional building blocks, and otherillustrative blocks, modules and components herein, can be implementedas illustrated or by discrete components, application specificintegrated circuits, processors executing appropriate software and thelike or any combination thereof.

The present invention may have also been described, at least in part, interms of one or more embodiments. An embodiment of the present inventionis used herein to illustrate the present invention, an aspect thereof, afeature thereof, a concept thereof, and/or an example thereof. Aphysical embodiment of an apparatus, an article of manufacture, amachine, and/or of a process that embodies the present invention mayinclude one or more of the aspects, features, concepts, examples, etc.,described with reference to one or more of the embodiments discussedherein. Further, from figure to figure, the embodiments may incorporatethe same or similarly named functions, steps, modules, etc., that mayuse the same or different reference numbers and, as such, the functions,steps, modules, etc., may be the same or similar functions, steps,modules, etc., or different ones.

While the transistors in the above described figure(s) is/are shown asfield effect transistors (FETs), as one of ordinary skill in the artwill appreciate, the transistors may be implemented using any type oftransistor structure including, but not limited to, bipolar, metal oxidesemiconductor field effect transistors (MOSFET), N-well transistors,P-well transistors, enhancement mode, depletion mode, and zero voltagethreshold (VT) transistors.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of the various embodimentsof the present invention. A module includes a processing module, afunctional block, hardware, and/or software stored on memory forperforming one or more functions as may be described herein. Note that,if the module is implemented via hardware, the hardware may operateindependently and/or in conjunction software and/or firmware. As usedherein, a module may contain one or more sub-modules, each of which maybe one or more modules.

While particular combinations of various functions and features of thepresent invention have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent invention is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by one or more processingmodules of one or more computing devices of a dispersed storage network(DSN), the method comprises: identifying a change in a resource level ofa plurality of resource levels of a DSN memory of the DSN, wherein thechange effects properties of at least one resource level of theplurality of resource levels, wherein the properties include resourceidentifiers and resource weighting coefficients for a plurality ofresources of the resource level prior to the change, and wherein dataobjects are dispersed storage error encoded to produce pluralities ofsets of encoded data slices that are stored in the DSN memory inaccordance with a distributed agreement protocol (DAP); and for encodeddata slices of the pluralities of sets of encoded data slices that areaffected by the change: obtaining updated properties of an updatedplurality of resources of the resource level based on the change,wherein the updated properties include updated resource identifiers forthe updated plurality of resources and updated resource weightingcoefficients for the updated plurality of resources, and wherein atleast some resources of the updated plurality of resources are differentfrom the plurality of resources; performing a first decentralizedagreement protocol function in accordance with the DAP using DSN accessinformation associated with the encoded data slices, the updatedresource identifiers, and the updated resource weighting coefficients toproduce updated storage scoring resultants for the encoded data slices,wherein the first decentralized agreement protocol function includes adeterministic function, a normalizing function, a scoring function, anda ranking function; utilizing a first updated storage scoring resultantof the updated storage scoring resultants to identify a first storageunit of the DSN that is to store at least one encoded data slice of theencoded data slices; performing a second decentralized agreementprotocol function of the DAP using the DSN access information associatedwith the set of encoded data slices, the resource identifiers of theplurality of resources, and the resource weighting coefficients for theplurality of resources to produce previous storage scoring resultantsfor the encoded data slices; utilizing a first previous storage scoringresultant of the previous storage scoring resultants to identify asecond storage unit of the DSN that is storing the at least one encodeddata slice; and sending at least one data migration request to at leastone of the first and second storage units to migrate the at least oneencoded data slice from the second storage unit to the first storageunit.
 2. The method of claim 1 wherein the DSN access informationincludes at least one of a source name, a slice name, and a DSN address.3. The method of claim 1 further comprises: sending another at least onedata migration request to at least one of a third storage unit of theDSN memory and the first storage unit to migrate another at least oneencoded data slice of the encoded data slices from the third storageunit to the first storage unit, wherein the third storage unit isidentified utilizing a second previous storage scoring resultant of theprevious storage scoring resultants.
 4. The method of claim 1, whereinthe sending the at least one data migration request to the at least oneof the first and second storage units comprises: migrating a set ofencoded data slices of the pluralities of sets of encoded data slicesfrom a set of storage units in a second storage pool of a plurality ofstorage pools of the DSN to an updated set of storage units in a firststorage pool of the plurality of storage pools, wherein the set ofstorage units includes the second storage unit and wherein the updatedset of storage units includes the first storage unit.
 5. The method ofclaim 1, wherein the sending the at least one data migration request tothe at least one of the first and second storage units comprises:migrating an encoded data slice of the at least one encoded data slicefrom a storage unit of an original set of storage units in a storagepool of a plurality of storage pools of the DSN to another storage unitof the original set of storage units, wherein the original set ofstorage units includes the first and second storage units.
 6. The methodof claim 1, wherein the sending the at least one data migration requestto the at least one of the first and second storage units comprises:migrating an encoded data slice of the at least one encoded data slicefrom a first memory device of the second storage unit to a second memorydevice of the second storage unit.
 7. The method of claim 1, wherein theperforming the first decentralized agreement protocol function furthercomprises: selecting the resource level of the plurality of resourcelevels; and selecting the updated properties from a plurality of updatedproperties of the DSN memory based on the selected resource level. 8.The method of claim 7 further comprises: calculating, based on theselected resource level, a plurality of storage values based on the DSNaccess information associated with the encoded data slices and theupdated properties of the selected resource level; and performing theranking function on the plurality of storage values to produce anupdated storage scoring resultant of the updated storage scoringresultants.
 9. The method of claim 1, wherein the change includes one ormore of: removing a storage pool; adding a new storage pool; removing astorage set; adding a new storage set; removing a storage unit; adding anew storage unit; decommissioning a memory; and commissioning a newmemory.
 10. The method of claim 1, where the identifying includes one ormore of: receiving DSN memory information; interpreting an errormessage; interpreting a commission report; interpreting a deactivationreport; interpreting configuration information; initiating a query; andinterpreting the received query response.
 11. A non-transitory computerreadable storage medium comprises: at least one memory section thatstores operational instructions that, when executed by one or moreprocessing modules of one or more computing devices of a dispersedstorage network (DSN), causes the one or more computing devices to:identify a change in a resource level of a plurality of resource levelsof a DSN memory of the DSN, wherein the change effects properties of atleast one resource level of the plurality of resource levels, whereinthe properties include resource identifiers and resource weightingcoefficients for a plurality of resources of the resource level prior tothe change, and wherein data objects are dispersed storage error encodedto produce pluralities of sets of encoded data slices that are stored inthe DSN memory in accordance with a distributed agreement protocol(DAP); and for encoded data slices of the pluralities of sets of encodeddata slices that are affected by the change: obtain updated propertiesof an updated plurality of resources of the resource level based on thechange, wherein the updated properties include updated resourceidentifiers for the updated plurality of resources and updated resourceweighting coefficients for the updated plurality of resources, andwherein at least some resources of the updated plurality of resourcesare different from the plurality of resources; perform, for the updatedplurality of resources, a first decentralized agreement protocolfunction in accordance with the DAP using DSN access informationassociated with the encoded data slices, the updated resourceidentifiers, and the updated resource weighting coefficients to produceupdated storage scoring resultants for the encoded data slices, whereinthe first decentralized agreement protocol function includes adeterministic function, a normalizing function, a scoring function, anda ranking function; utilizing a first updated storage scoring resultantof the updated storage scoring resultants to identify a first storageunit of the DSN that is to store at least one encoded data slice of theencoded data slices; perform a second decentralized agreement protocolfunction of the DAP using the DSN access information associated with theset of encoded data slices, the resource identifiers of the plurality ofresources, and the resource weighting coefficients for the plurality ofresources to produce previous storage scoring resultants for the encodeddata slices, level of the plurality of resource the previous pluralityof resources; utilizing a first previous storage scoring resultant ofthe previous storage scoring resultants to identify a second storageunit of the DSN that is storing the at least one encoded data slice; andsend at least one data migration request to at least one of the firstand second storage units to migrate the at least one encoded data slicefrom the second storage unit to the first storage unit.
 12. Thenon-transitory computer readable storage medium of claim 11, wherein theDSN access information includes at least one of a source name, a slicename, and a DSN address.
 13. The non-transitory computer readablestorage medium of claim 11 further comprises: the at least one memorysection stores further operational instructions that, when executed bythe one or more processing modules, causes the one or more computingdevices of the DSN to: send another at least one data migration requestto at least one of a third storage unit of the DSN memory and the firststorage unit to migrate another at least one encoded data slice of theencoded data slices from the third storage unit to the first storageunit, wherein the third storage unit is identified utilizing a secondprevious storage scoring resultant of the previous storage scoringresultants.
 14. The non-transitory computer readable storage medium ofclaim 11, wherein the one or more processing modules functions toexecute the operational instructions stored by the at least one memorysection to cause the one or more computing devices of the DSN to sendthe at least one data migration request to the at least one of the firstand second storage units by: migrating a set of encoded data slices ofthe pluralities of sets of encoded data slices from a set of storageunits in a second storage pool of a plurality of storage pools of theDSN to an updated set of storage units in a first storage pool of theplurality of storage pools, wherein the set of storage units includesthe second storage unit and wherein the updated set of storage unitsincludes the first storage unit.
 15. The non-transitory computerreadable storage medium of claim 11, wherein the one or more processingmodules functions to execute the operational instructions stored by theat least one memory section to cause the one or more computing devicesof the DSN to send the at least one data migration request to the atleast one of the first and second storage units by: migrating an encodeddata slice of the at least one encoded data slice from a storage unit ofan original set of storage units in a storage pool of a plurality ofstorage pools of the DSN to another storage unit of the original set ofstorage units, wherein the original set of storage units includes thefirst and second storage units.
 16. The non-transitory computer readablestorage medium of claim 11, wherein the one or more processing modulesfunctions to execute the operational instructions stored by the at leastone memory section to cause the one or more computing devices of the DSNto send the at least one data migration request to the at least one ofthe first and second storage units by: migrating an encoded data sliceof the at least one encoded data slice from a first memory device of thesecond storage unit to a second memory device of the second storageunit.
 17. The non-transitory computer readable storage medium of claim11, wherein the one or more processing modules functions to execute theoperational instructions stored by the at least one memory section tocause the one or more computing devices of the DSN to perform the firstdecentralized agreement protocol function further by: selecting theresource level of the plurality of resource levels; and selecting theupdated properties from a plurality of updated properties of the DSNmemory based on the selected resource level.
 18. The non-transitorycomputer readable storage medium of claim 17 further comprises: the atleast one memory section stores further operational instructions that,when executed by the one or more processing modules, causes the one ormore computing devices of the DSN to: calculate, based on the selectedresource level, a plurality of storage values based on the DSN accessinformation associated with the encoded data slices and the updatedproperties of the selected resource level; and perform the rankingfunction on the plurality of storage values to produce an updatedstorage scoring resultant of the updated storage scoring resultants.